Methods for efficient cluster analysis

ABSTRACT

Some embodiments provide a method for defining structure for an unstructured document that includes a number of primitive elements that are defined in terms of their position in the document. The method identifies a pairwise grouping of nearest primitive elements. The method sorts the pairwise primitive elements based on an order from the closest to the furthest pairs. The method stores a single value that identifies which of the pairwise primitive elements are sufficiently far apart to form a partition. The method uses the stored value to identify and analyze the partitions in order to define structural elements for the document.

CLAIM OF BENEFIT TO PRIOR APPLICATION

This application claims the benefit of U.S. Provisional Application61/142,329, entitled “Methods and System for Document Reconstruction”,filed Jan. 2, 2009, which is incorporated herein by reference.

FIELD OF THE INVENTION

The invention is directed towards methods for efficient clusteranalysis. Specifically, the invention is directed towards methods fordefining a structured document from an unstructured document, forimproving the efficiency of such processes, and for improving display ofand interaction with structured documents.

BACKGROUND OF THE INVENTION

Documents are often defined as nothing more than a collection ofprimitive elements that are drawn on a page at defined locations. Forexample, a PDF (portable document format) file might have no definitionof structure and instead is nothing more than instructions to drawglyphs, shapes, and bitmaps at various locations.

A user can view such a document on a standard monitor and deduce thestructure. However, because such a file is only a collection ofprimitive elements, a document viewing application has no knowledge ofthe intended structure of the document. For example, a table isdisplayed as a series of lines and/or rectangles with text between thelines, which the human viewer recognizes as a table. However, theapplication displaying the document has no indication that the textgroupings have relationships to each other based on the rows and columnsbecause the document does not include such information. Similarly, theapplication has no indication of the flow of text through a page (e.g.,the flow from one column to the next, or the flow around an embeddedimage), or various other important qualities that can be determinedinstantly by a human user.

This lack of knowledge about document structure will not always be aproblem when a user is simply viewing the document on a standardmonitor. However, it would often be of value to a reader to be able toaccess the file and edit it as though it were a document produced by aword processor, image-editing application, etc., that has structure andrelationships between elements. Therefore, there is a need for methodsthat can reconstruct an unstructured document. Similarly, there is aneed for methods that take advantage of such reconstructed documentstructure to idealize the display of the document (e.g., forsmall-screen devices where it is not realistic to display the entiredocument on the screen at once), or to enable intelligent selection ofelements of the document.

In the modern world, more and more computing applications are moving tohandheld devices (e.g., cell phones, media players, etc.). Accordingly,document reconstruction techniques must be viable on such devices, whichgenerally have less computing power than a standard personal computer.However, document reconstruction often uses fairly computation andmemory intensive procedures, such as cluster analysis, and the use oflarge chunks of memory. Therefore, there is further a need fortechniques that allow for greater efficiency in document reconstructiongenerally, and cluster analysis specifically.

SUMMARY OF THE INVENTION

Different embodiments of the invention use different techniques foranalyzing an unstructured document to define a structured document. Insome embodiments, the unstructured document includes numerous primitiveelements, but does not include structural elements that specify thestructural relationship between the primitive elements and/or structuralattributes of the document based on these primitive elements.Accordingly, to define the structured document, some embodiments use theprimitive elements of the unstructured document to identify variousgeometric attributes of the unstructured document, and then use theidentified geometric attributes and other attributes of the primitiveelements to define structural elements, such as associated primitiveelements (e.g., words, paragraphs, joined graphs, etc.), tables, guides,gutters, etc., as well as to define the flow of reading through theprimitive and structural elements.

As mentioned, some embodiments use primitive elements to identifyvarious geometric attributes. For instance, some embodiments provide amethod that identifies boundaries between sets of primitive elements andregions bounded by the boundaries. The method uses the identifiedregions to define structural elements for the document, and defines astructured document based on the primitive elements and the structuralelements. In some embodiments, defining structural elements includesanalyzing each region separately to create associations between sets ofprimitive elements in the particular region. In some embodiments,defining the structured document includes identifying hierarchicalrelationships between the identified regions.

Some embodiments provide a method that analyzes an unstructured documentthat includes numerous words, where each word is an associated set ofglyphs and each glyph has location coordinates. The method identifiesclusters of location values, where each location value is associatedwith one word, is a basis for word alignment, and is derived from thelocation coordinates of the glyphs of that word. Based on the identifiedclusters of location values, the method defines a set of boundaryelements for the words that identify a set of alignment guides for thewords. The method defines a structured document based on the glyphs andthe defined boundary elements. Some embodiments also define at least oneregion of white space between a pair of boundary elements and furtherdefine the structured document based on the region of white space. Someembodiments identify the clusters of location values by using densityclustering.

Some embodiments use the identified geometric attributes and otherattributes of the primitive elements to define structural elements aswell as to define the flow of reading through the primitive andstructural elements. For instance, some embodiments provide a methodthat analyzes an unstructured document that includes numerous glyphs,each of which has a position in the unstructured document. Based on thepositions of glyphs, the method creates associations between differentsets of glyphs in order to identify different sets of glyphs asdifferent words. The method creates associations between different setsof words in order to identify different sets of words as differentparagraphs. The method defines associations between paragraphs that arenot contiguous in order to define a reading order through theparagraphs. In order to create associations between different sets ofwords in order to identify different sets of words as differentparagraphs, some embodiments create associations between different setsof words as different text lines, and create associations betweendifferent sets of text lines as different paragraphs.

Some embodiments provide a method that identifies boundaries betweensets of glyphs and identifies that several of the boundaries form atable. The method defines a tabular structural element based on thetable that includes several cells arranged in several rows and columns,where each cell includes an associated set of glyphs. Some embodimentsidentify that the boundaries form a table by identifying a set ofboundaries that form a larger rectangular shape and several rectangularshapes contained within the larger rectangular shape. In someembodiments, at least some of the identified boundaries are inferredbased on positions of the associated sets of glyphs that form the cells.

Some embodiments provide a method for analyzing an unstructured documentthat includes numerous primitive graphic elements, each of which isdefined as a single object. The document has a drawing order thatindicates the order in which the primitive graphic elements are drawn.The method identifies positional relationships between successiveprimitive graphic elements in the drawing order. Based on the positionalrelationships, the method defines a single structural graphic elementfrom several primitive graphic elements. Some embodiments identify apositional relationship between a first and second primitive graphicelement that are subsequent in the drawing order by calculating a sizeof a structural graphic element that includes the first and secondprimitive graphic elements.

Some embodiments provide methods to make geometric analysis and documentreconstruction more effective. For instance, some embodiments provide amethod that provides a default set of document reconstruction operationsfor defining a structured document that comprises a plurality ofprimitive elements. The method provides a hierarchical set of profiles,each profile including (i) a set of document reconstruction results and(ii) results for modifying the document reconstruction operations whenintermediate document reconstruction results match the potentialdocument reconstruction results for the profile. Instructions from aprofile at a lower level in the hierarchy override instructions from aprofile at a higher level. In some embodiments, the instructions for aparticular profile include a subset of profiles at a lower level in thehierarchical set of profiles that should be tested when the intermediatedocument reconstruction results match the potential documentreconstruction results for the profile.

Once a structured document is defined, some embodiments provide varioustechniques for idealizing user interaction with the structured document.For instance, some embodiments provide a method for displaying astructured document that includes a hierarchy of structural elementsconstructed by analyzing an unstructured document. The method displaysthe structured document on the device (e.g., a small-screen device). Themethod receives a position of interest in the document, and identifies astructural element within the hierarchy as a region of interest based onthe position of interest. The method modifies the display of thedocument to highlight the identified region of interest. Someembodiments identify the structural element by identifying a structuralelement at the lowest level of the hierarchy that includes the positionof interest, and identifying structural elements at higher levels ofhierarchy that include the structural element identified at the lowestlevel until a structural element qualifying as a region of interest isreached. Some embodiments also receive an input to move from the regionof interest and modify the display of the document to highlight astructurally related region of interest.

Some embodiments provide a method for defining a selection of text in anunstructured document that includes numerous glyphs. The methodidentifies associated sets of glyphs and a reading order that specifiesa flow of reading through the glyphs. The method displays the documentand receives a start point and end point for a selection of text withinthe displayed document. The method defines the selection of text fromthe start point to the end point by using the identified sets of glyphsand intended flow of reading. In some embodiments, the associated setsof glyphs are paragraphs and the reading order specifies a flow ofreading from a first paragraph to a second paragraph that are notcontiguous.

Some embodiments provide methods that enhance the efficiency of thegeometric analysis and document reconstruction processes. Someembodiments use cluster analysis for geometric analysis and/or documentreconstruction, which can be a computing-intensive process. Accordingly,some embodiments provide a method that defines structure for anunstructured document that includes numerous primitive elements that aredefined in terms of their position in the document. The methodidentifies a pairwise grouping of nearest primitive elements and sortsthe pairwise primitive elements based on an order from the closest tothe furthest pairs. The method stores a single value that identifieswhich of the pairwise primitive elements are sufficiently far apart toform a partition. The method uses the stored value to identify andanalyze the partitions in order to define structural elements for thedocument.

Some embodiments also provide methods for making use of efficient datastructures. For instance, some embodiments provide several differentprocesses for analyzing and manipulating an unstructured document thatincludes numerous primitive elements. Some embodiments also provide astorage for data associated with the primitive elements. At least someof the data is stored in a separate memory space from the processes andis shared by at least two different processes. The processes access thedata by use of references to the data. The data is not replicated by theprocesses.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features of the invention are set forth in the appendedclaims. However, for purpose of explanation, several embodiments of theinvention are set forth in the following figures.

FIG. 1 illustrates the overall reconstruction flow of some embodiments.

FIG. 2 illustrates a page of a document and various results fromgeometric analysis and document reconstruction of some embodiments beingperformed on the page.

FIG. 3 conceptually illustrates a process of some embodiments foridentifying zones of a page of a document and generating a zone tree forthe page.

FIG. 4 illustrates a page and a sequence of identifying zones of thepage and generating a zone tree for the page in some embodiments.

FIG. 5 illustrates a page of a document that includes several zones.

FIG. 6 illustrates a page that includes zone border graphics andmultiple zones, including rotation groups.

FIG. 7 illustrates a zone tree of some embodiments for the page fromFIG. 5

FIG. 8 conceptually illustrates a process of some embodiments fordefining rotation groups on a page.

FIG. 9 conceptually illustrates a process of some embodiments foridentifying zone borders and intersections.

FIG. 10 illustrates a page that includes various graphics and text.

FIG. 11 illustrates the zone border intervals and intersections for thepage of FIG. 10.

FIG. 12 conceptually illustrates a process of some embodiments foridentifying zones.

FIGS. 13 and 14 illustrates the application of the process of FIG. 12 toidentify the zones of the page of FIG. 10.

FIG. 15 conceptually illustrates a process of some embodiments forgenerating a zone tree.

FIG. 16 illustrates the zones from the page of FIG. 10 sorted by sizeand placed into a node graph.

FIG. 17 conceptually illustrates the software architecture of a zoneanalysis application of some embodiments.

FIG. 18 illustrates an overall process of some embodiments foridentifying guides and gutters in a document.

FIG. 19 illustrates a page having two columns of text, and the guidesand gutters identified for the page.

FIG. 20 conceptually illustrates a process of some embodiments forperforming density clustering.

FIG. 21 conceptually illustrates a process of some embodiments fordetermining left-alignment guides.

FIGS. 22-24 illustrate the identification a left-alignment guide on apage.

FIG. 25 conceptually illustrates a process of some embodiments fordetermining right-alignment guides.

FIG. 26 conceptually illustrates a process of some embodiments fordetermining gutters for a region of a document.

FIGS. 27-29 illustrate the identification of a gutter on a page.

FIG. 30 conceptually illustrates the software architecture of a guideand gutter analysis application of some embodiments.

FIG. 31 conceptually illustrates a process of some embodiments fordetermining the layout and flow of a document.

FIG. 32 illustrates a sequence of some embodiments of the determinationof layout and flow information for a page of a document.

FIG. 33 conceptually illustrates a process of some embodiments foridentifying and merging lines of text.

FIG. 34 illustrates a page with six groups of overlapping text lines.

FIG. 35 illustrates the merging of the groups of text lines from FIG.34.

FIG. 36 conceptually illustrates a process of some embodiments forperforming difference clustering.

FIG. 37 illustrates an example of difference clustering.

FIG. 38 conceptually illustrates a process of some embodiments forsplitting lines of text.

FIG. 39 illustrates a sequence showing the identification of where tosplit lines of text on a page.

FIG. 40 conceptually illustrates a process of some embodiments forgrouping text lines into paragraphs.

FIG. 41 illustrates the identification of paragraphs on a page.

FIG. 42 conceptually illustrates a process of some embodiments foridentifying columns and layouts in a portion of a document.

FIGS. 43 and 44 illustrate paragraphs on two different pages.

FIGS. 45 and 46 illustrate the generation of flow graphs for the pagesof FIGS. 43 and 44.

FIG. 47 conceptually illustrates the software architecture of a layoutand flow analysis application of some embodiments.

FIG. 48 conceptually illustrates a process of some embodiments forjoining individual graphs into joined graphs.

FIG. 49 illustrates the joining of graphs on a page.

FIG. 50 conceptually illustrates a process of some embodiments forperforming bounds clustering to identify graphs that should be joinedand joining those graphs.

FIG. 51 illustrates two pages, each having two graphic objects for whichthe spread is calculated.

FIG. 52 illustrates a process of some embodiments for processing acluster into subsequences.

FIG. 53 conceptually illustrates a graph joining application of someembodiments for identifying graphs that should be joined and associatingthe graphs as one graphic.

FIG. 54 conceptually illustrates a process of some embodiments forsemantically reconstructing a document on a limited-resource deviceusing cluster analysis.

FIG. 55 illustrates a sequence of some embodiments by which a documentis semantically reconstructed.

FIG. 56 conceptually illustrates a process of some embodiments forpartitioning a data set by using indirectly sorted arrays.

FIG. 57 illustrates the partitioning of a data set with nine data items.

FIG. 58 conceptually illustrates a process of some embodiments forperforming cluster analysis at multiple distance scales concurrently.

FIG. 59 conceptually illustrates the software architecture of a clusteranalysis application of some embodiments for performing clusteranalysis.

FIG. 60 conceptually illustrates a process of some embodiments forreconstructing a document efficiently.

FIG. 61 illustrates a sequence by which a document is parsed andanalyzed according to the process of FIG. 60.

FIG. 62 illustrates the manner in which data is stored according to someembodiments of the invention.

FIG. 63 conceptually illustrates an API that performs documentreconstruction processes while using efficient memory managementtechniques.

FIG. 64 conceptually illustrates the software architecture of anapplication of some embodiments for reconstructing, displaying, andinteracting with a document.

FIG. 65 conceptually illustrates a process of some embodiments formanufacturing a computer readable medium that stores a computer programsuch as the application described in FIG. 64.

FIG. 66 conceptually illustrates a computer system with which someembodiments of the invention are implemented.

DETAILED DESCRIPTION OF THE INVENTION

In the following description, numerous details are set forth for purposeof explanation. However, one of ordinary skill in the art will realizethat the invention may be practiced without the use of these specificdetails. For instance, in some cases, the techniques described below aredescribed as taking place in a specific order. However, in someembodiments, the techniques are performed in an order different fromthat described. Furthermore, while the techniques are described forlanguages that are read left-to-right (e.g., English), one of ordinaryskill will recognize that the techniques are easily adapted forright-to-left languages.

I. Overview

Some embodiments of the invention provide novel methods for defining astructured document from an unstructured document. In some embodiments,an unstructured document is a document defined to include only primitiveelements such as shapes (e.g., vector graphics), images (e.g., bitmaps),and glyphs. In some embodiments, a glyph is a visual representation of atext character (e.g., a letter, a number, a punctuation mark, or otherinline character), collection of characters, or portion of a character.In some embodiments, a glyph may be a pre-specified collection ofscalable vector graphics including path definitions for the outline ofthe glyph. In some embodiments, a glyph may be a pre-specified rasterimage or collection of raster images optimized for various sizes. As anexample, the character “i” could be represented by a single glyph thatis a path with two sub-paths, one for the outline of the dot and one forthe outline of the lower portion. As another example, the combination ofthree characters “ffi”, when occurring in sequence, are sometimesrepresented by a single glyph called a ligature, drawn in a slightlydifferent manner than the characters occurring individually. As a thirdexample, accented characters such as “ê” are sometimes represented bymore than one glyph (e.g. one for the character and one for the accent)and are sometimes represented by a single glyph (combining accent withcharacter).

The unstructured document of some embodiments does not specify anyrelationship or association between the primitive elements, while inother embodiments it specifies a minimum amount of such relationshipsand associations. In some embodiments, the unstructured document mayhave some amount of structure, but the structure is unrecognizable ornot relied upon. In some embodiments the unstructured document has anunknown structure or is assumed to be unstructured.

Some embodiments generate, from the unstructured document, a structureddocument that includes associations and relationships between theprimitive elements, groupings and orderings of the primitive elements,and properties of the groups of primitive elements. For instance, someembodiments use the primitive elements of the unstructured document toidentify various geometric attributes of the unstructured document anduse these identified geometric attributes (along with other attributesof the primitive elements) to define structural elements. Structuralelements of some embodiments include associated primitive elements(e.g., words, paragraphs, joined graphs, etc.), guides, gutters, textflow, tables, etc. These structural elements are related in ahierarchical manner in some embodiments (e.g., a paragraph includes textlines, a text line includes words, and a word includes primitiveglyphs). In some embodiments, the structured document serves twopurposes—it identifies associated elements (e.g., the elements making upa table) and it identifies a flow order through the primitive elements(i.e., the order in which a human would be expected to read through theprimitive elements in the document).

Upon receiving an unstructured document, some embodiments first parsethe document into its constituent elements (e.g., primitive elements andtheir associated information such as coordinate locations, drawingorder, etc.). For instance, a large block of text might be defined inthe unstructured document as a number of character glyphs, each havingx- and y-coordinates at which their anchors are placed on a particularpage along with a scale factor determining the size of each glyph (andany other linear transforms that are to be applied), each glyph to bedrawn on the page in a particular order (relevant to the compositingoperation performed when one glyph overlays another). Some embodimentsthen perform geometric analysis on the primitive elements to definegeometric attributes of the document. For example, some embodimentsanalyze the primitive elements to identify boundaries between primitiveelements and regions bordered by the boundaries.

FIG. 1 illustrates the overall flow of some embodiments. As shown, adocument 100 is initially (after parsing to identify the primitiveelements, in some embodiments) analyzed by the geometric analysismodules 110. Geometric analysis modules 110 analyze a document toidentify geometric attributes such as boundaries and regions bordered bythe boundaries. In some embodiments, the regions include zones that arebordered by primitive elements such as straight lines and narrowrectangles (i.e., particular primitive shapes and images).

FIG. 2 illustrates a page 200 of an incoming document and variousresults from geometric analysis and document reconstruction. Theincoming document is an unstructured document that has a collection ofprimitive elements that a human viewing the document would recognize astext, borders, a table, and a graphic object. Analysis result 205illustrates that the geometric analysis modules 110 have recognized twozones Z₁ 240 and Z₂ 245 separated by boundaries 250 in document 200.

In some embodiments, the boundaries identified by geometric analysismodules 110 also include alignment guides. In some embodiments, analignment guide is a vertical edge formed by the beginning or end ofwords (e.g., at the left edge of a column of left-aligned text).Similarly, in some embodiments, the regions identified by geometricanalysis include gaps of unfilled white space between groups of glyphs(e.g., between guides). These gaps are called gutters in someembodiments.

Analysis result 210 illustrates a left-alignment guide 212 at the leftedge of the first column of text and a gutter 214 spanning the whitespace between the two columns of text (for simplicity, the other guidesand the columns of text are not shown). As illustrated in FIG. 1, theoutput of the semantic analysis modules 110 of some embodiments is zones105, guides 115, and gutters 125.

The data output from geometric analysis modules 110 is sent to documentreconstruction modules 120. Document reconstruction modules 120 continuethe process of analyzing the unstructured document to define astructured document. In some embodiments, document reconstructionmodules 120 create associations between primitive elements in order todefine contiguous structural elements such as text, tables, and shapes.Some embodiments also define a hierarchy of the structural elements andrelationships between the structural elements.

For instance, in some embodiments, the document reconstruction modules120 create associations between glyphs, sets of glyphs, sets of sets ofglyphs, etc. Some embodiments associate individual glyphs into words,words into text lines, text lines into paragraphs, etc. Analysis result215 illustrates that individual lines 217 and paragraphs 219 areidentified within the first column of text.

The document reconstruction modules 120 also identify the layout ofglyphs in order to define the text flow through the glyphs.Specifically, to define the text flow, some embodiments identify areading order through the glyphs (or through the sets of glyphs), whichrepresents the order in which a human would be expected to read throughthe glyphs on a page (e.g., from the bottom of a first column to the topof a second column, then skipping a separated text box in the center,etc.) Analysis result 220 illustrates that two columns are identifiedwithin the document 200 and that the reading flow 222 runs from thebottom of the first column to the top of the second column. In someembodiments, the identification and definition of layout and flow makesuse of the zone results 205, the guide and gutter results 210, and theglyph association results 215.

The document reconstruction modules 120 also define other structuralelements in a document that are associations between primitive elementsother than glyphs or between structural elements. For instance, in someembodiments, document reconstruction modules 120 identify tables in adocument as associations between regions identified by geometricanalysis modules 110 as well as the glyphs and sets of glyphs within theregions. For example, some embodiments associate regions as cells of atable, and the glyphs inside each region as the table information.Analysis result 225 illustrates the identification of a table 227 withnine cells 229 in document 200 by document reconstruction modules 120.Some embodiments associate the primitive elements that form the table bydefining a tabular structural element. Whereas in the initial document,what was viewed as a table was defined as an unassociated collection ofprimitive elements (lines and glyphs), after reconstruction the cellsare identified in the tabular structural element as table cells and areindividually or collectively editable. As further illustrated, in someembodiments, the table identification and reconstruction uses zoneresults 205, glyph association results 215, and layout and flow results220.

Some embodiments also identify when two or more primitive graphicelements or graphic objects (e.g., shapes, images, photographs, bitmaps,etc.) in the document should be grouped as one structural graphicelement. For instance, two objects that mostly overlap may be oneelement that is defined as two shapes or images in the unstructureddocument. The document reconstruction modules 120 join these two objectsas one object. Analysis result 230 illustrates that the two primitiveshapes (a star and a hexagon) from the initial document 200 have beenjoined as one graphic 232 by the document reconstruction modules 120.

As illustrated in FIG. 1, examples of the output of the documentreconstruction modules 120 include semantic hierarchy data 135 (i.e.,associations of glyphs), layout and flow data 145, table data 155, andjoined graph data 165. Furthermore, in some embodiments, some of thisinformation is also passed between the several document reconstructionmodules 120. FIG. 2 illustrates that all of this information is used todefine a structured document 235. Structured document 235 has the sameappearance as unstructured document 200, but the structured document 235includes information about the structural elements and the associations,relationships, and hierarchy of elements, thereby enabling editing, moreintuitive display, etc.

The data from the document reconstruction modules 120 (as well as, insome embodiments, data from the geometric analysis modules 110) is usedby document display and interaction modules 130. Document display andinteraction modules 130 enable a user to view, edit, scroll through,etc. a document. For example, sequence 140 illustrates a documentdisplayed as two columns of text on a handheld device that is heldupright. When the handheld device is rotated on its side, the text inthe two columns is rearranged into three columns. This rearrangementcannot be done with an unstructured document, because it relies upon theassociations between elements, especially the flow of text throughglyphs that is not part of the unstructured document.

In some embodiments, document display and interaction modules 130 canalso recognize a structural element (e.g., a paragraph, graphic object,etc.) that has been selected by a user and intelligently zoom to displaythe selected element. In some embodiments, the user selects a positionof interest (i.e., a particular location in a displayed document), andthe display and interaction modules 130 identify a qualifying structuralelement in the hierarchy of structural elements. Some embodiments defineparticular types of structural elements as qualifying structuralelements. The qualifying structural element is used to define a regionof interest that is highlighted in the display in some embodiments.

Sequence 150 illustrates a selection of a paragraph 170 (e.g., by aselection of a position of interest of interest within the paragraph)and the subsequent intelligent display of the paragraph and nearby text.Document display and interaction modules 130 also provide other featuressuch as intelligent selection of text and graphic objects, intelligentscrolling through a document, etc.

Some embodiments use hierarchical profiling to modify how geometricanalysis and document reconstruction are performed on the fly, usingintermediate analysis and reconstruction results. Some embodiments checkthe intermediate results against profiles that indicate what type ofcontent a document includes and alter the reconstruction processesaccordingly. In some embodiments, the hierarchical profiles can instructthe analysis and reconstruction modules to perform more or lessprocesses, perform processes differently, or re-perform processes. Forinstance, if intermediate analysis results indicate that a document isone page long, has one column of text, and no shapes or images, thensome embodiments will only perform processes to associate the glyphsinto words, lines, and paragraphs. Table identification, for instance,will not be performed.

Some embodiments employ various novel efficiency techniques for moreefficient memory and processing usage. For instance, some embodimentsperform some of the above described processes by using cluster analysis,which is a technique used to identify groups of elements that areclosely spaced in some way relative to other elements. Some embodimentsuse cluster analysis to identify guides based on numerous words startingat, ending at, centered on or otherwise aligned with the same or nearlythe same x-coordinate. Some embodiments use cluster analysis torecognize different size gaps between glyphs so as to identify gapsbetween words and gaps larger than those between words. Some embodimentsalso use cluster analysis to identify primitive graphics (e.g., shapes,images) that should be joined into single graphics.

Some embodiments perform cluster analysis efficiently by using ordereddata (e.g., primitive element position data) that references unsorteddata, and by storing partitions of the data using a single value. Apartition, as this term is used in the present invention, divides asequence, or linearly ordered set, into subsequences, which are subsetsof the sequence with the same order relation. Furthermore, a partitionhas the properties that (i) every member of the original sequence iscontained in exactly one of the partition's subsequences, and (ii) giventwo of the partition's subsequences S and T, either all the members of Sare less than all the members of T or all the members of T are less thanall the members of S, according to the order relation. Storing apartition as a single value enables various cluster analysis functions,such as examining multiple partitions, to be performed more efficientlyin some embodiments.

Some embodiments also gain efficiency in the document reconstructionprocess by using an application programming interface (API) thatminimizes the amount of copying of data while appearing to the user ofthe API (e.g., a programmer or a software application using the API) asthough the data is freely modifiable. Some embodiments store data in arandomly ordered array, then define a sorted array of references to thedata and share this sorted array among numerous collection objects (e.g.character sequence objects, which are collections of character data) tooptimize the usage of memory and processing. Both of these efficiencyenhancements, as well as others, are used in some embodiments to enabledocument reconstruction to be performed on a limited-resource device,such as a cell phone, media player, etc. (e.g., an iPhone®).

Although the above-described overview of some embodiments was providedby reference to the examples illustrated in FIGS. 1 and 2, one ofordinary skill will realize that these examples were meant only asexemplary embodiments that introduced the features and operations ofsome embodiments of the invention. One of ordinary skill will realizethat many embodiments have features and operations that are differentthan those illustrated in FIGS. 1 and 2. For instance, althoughgeometric analysis has been described as one set of modules 110, one ofordinary skill would recognize that some embodiments do not necessarilyidentify all geometric attributes at once. For example, some embodimentsdo a subset of geometric analysis first (e.g., region analysis toidentify one or more zones in the document) and then guides and guttersare identified on a zone-by-zone basis.

More detailed examples of some embodiments will be described below.Section II describes the identification of regions (i.e., zones) of adocument based on boundary primitive elements and the definition of ahierarchical structure (e.g., a document object model) that forms theframework of a structured document. Section III then describes theidentification of boundary elements for glyphs (e.g., alignment guides)and particular empty spaces between alignment points (gutters). Next,Section IV details the creation of associations between glyphs and setsof glyphs to define structural elements such as words, text lines,paragraphs, columns, etc., as well as the definition of a flow orderthrough these structural elements (as well as other elements such asgraphics, tables, etc.). Section V describes the identification ofprimitive graphic elements that should be grouped together and thecreation of associations between such primitive elements to definecompound graphic elements.

Section VI then describes various methods for improving the efficiencyof cluster analysis techniques, which (among other uses) are used foridentification of alignment guides, words and glyph spacing, andcompound graphics in the document reconstruction process. Next, SectionVII details methods and data structures that enable more efficientparsing and analysis of a document. These data structures illustrate onemanner of creating associations between glyphs (e.g., to form words,text lines, paragraphs, etc.) that can be used in the documentreconstruction process. However, one of ordinary skill in the art willrecognize that many other ways of creating associations betweenprimitive elements (e.g., glyphs, graphic elements, etc.) to definestructural elements (e.g., paragraphs, tables, compound graphics, etc.)are possible, as is well known in the art. Next, Section VIII describesthe software architecture of a document reconstruction application ofsome embodiments, and Section IX describes a computer system thatimplements some embodiments of the invention.

II. Zone Analysis

When there are multiple articles, sections or categories of informationon a page, these are often delineated by lines, images or shapes.Although a human can easily identify the manner in which graphical cuesare intended to indicate how the page is broken up into zones, this is anontrivial problem for a computer (particularly in the presence of amixture of graphic primitive elements, some of which are intended aspage content while others are intended to delineate content zones).

Some embodiments of the invention provide methods for identifyingboundaries and the regions bordered by those boundaries (e.g., zones)based on the primitive elements (e.g., the shapes and images) of anunstructured document. In some embodiments, the regions are used insubsequent reconstruction of the document as well as forcompartmentalization of further reconstruction processes. Someembodiments generate a region graph (i.e., hierarchical structure suchas a tree) that is populated with content and enables the association ofcontent with the region in which the content is located. Someembodiments perform the region identification on a page-by-page basis.

FIG. 3 conceptually illustrates a process 300 for identifying zones of apage of a document and generating a zone tree for the page in someembodiments. Process 300 will be described in conjunction with FIG. 4.FIG. 4 illustrates a page the sequence of identifying zones of a page400 of a document and generating a zone tree 430 for the page accordingto some embodiments. As shown in FIG. 3, process 300 begins by receiving(at 305) a page of a document. In some cases a document includesnumerous pages (e.g., an e-book), whereas in other cases a document willonly be one page (e.g., an advertisement flyer).

Next, the process identifies (at 310) zones on the page. In someembodiments, the identification of zones includes identifying zoneborders and intersections and then traversing the zone borders toidentify the zones. Referring to the example of FIG. 4, process 300identifies that page 400 includes five zones: zones A 405, B 410, C 415,D 420, and E 425.

After identifying the zones, process 300 generates (at 315) a zone graph(i.e., hierarchical structure such as a tree) for the page. The zonegraph illustrates the hierarchy of the zones. For instance, zone tree430 illustrates that a zone for the page (node P) includes four zones A,B, C, and D. Furthermore, zone D includes zone E, as zone E is fullywithin zone D. In some embodiments, a first zone is the parent of asecond zone when the second zone is wholly within the first zone. Aparent and a child can share one or more borders in some embodiments.

After generating the zone graph, process 300 inserts (at 320) thecontent of the page into the zone graph. The process then ends. In someembodiments, a page includes text, graphics, or other content. Eachparticular content grouping (e.g., an image, paragraph, column, etc.) isplaced as a child of the smallest zone that fully contains theparticular content grouping. In some embodiments, the insertion ofcontent objects into the zone graph is performed later in the documentreconstruction process, once the content has been further analyzed(e.g., grouping text into paragraphs, identifying tables, etc.).Furthermore, as document reconstruction is performed, some embodimentsupdate the zone graph with content subtrees for each zone.

A. Terminology

FIG. 5 illustrates a page 500 of a document that includes several zones.Page 500 includes numerous zone borders, including zone borders 505-509.Zone borders, in some embodiments, are horizontal or vertical (i.e.,rectilinear) strips with a thickness defined by the zone border graphicsthat contribute to the zone border. The thickness of a zone border, insome embodiments, is the width, in its narrow direction, of an uprightbounding box of the zone border graphics that contribute to the zoneborder. In some embodiments, an upright bounding box for a particularelement or set of elements is the smallest upright rectangle (in thecoordinate system being analyzed) that fully envelops the element or setof elements.

Zone border graphics are graphic objects (e.g., shapes, images, lines)on a page that either are narrow rectangles or have an upright boundingbox that is a narrow rectangle For instance, zone borders 505-509 areall lines with a particular (relatively narrow) thickness. In someembodiments, zone border graphics include relatively narrow objects, allor part of the rendering of which fills all or part of a zone border. Insome embodiments, zone border graphics also include objects whoseboundary contributes to a zone border (e.g., one side of a filledpolygon can indicate all or part of a zone border even though thepolygon itself is not narrow and does not fit in the border bounds).

Zone borders graphics, however, need not be perfectly straight lines orperfectly rectilinear. For instance, FIG. 6 illustrates a page 600 thatincludes zone border graphics 605. Zone border graphics 605 are notperfectly vertical strips: instead they are images of twigs that arealigned very close to vertically. Some embodiments will recognize thegraphic as a zone border graphic, whereas some embodiments will not.

Page 500 of FIG. 5 also includes numerous zone border intersections,such as intersections 510 and 511. In some embodiments, a zone borderintersection is a rectangular intersection of a horizontal zone borderwith a vertical zone border. As intersection 511 illustrates, a zoneborder intersection need not be at the end of a zone border. Zone borderintersections in the middle of a zone border break the zone border intoone or more zone border intervals, in some embodiments. For instance,the bottom zone border of page 500 is broken into zone border intervals515, 516, 517, and 518.

A zone, therefore, is a closed region bounded by a collection of zoneborder intervals that form an upright rectilinear shape in someembodiments. Upright rectilinear shapes are any polygons that can beformed by horizontal and vertical line segments, including but notlimited to upright rectangles, which are rectangles formed fromhorizontal and vertical line segments. Each zone has an uprightrectilinear outer bound which is a shape formed from the outer sides ofits zone border bounding rectangles. Each zone also has an uprightrectilinear inner bound, which is a shape formed from the inner sides ofits zone border bounding rectangles.

Page 500 includes zones P 526 (the page bounds), A 520 (an arch-shapedzone that includes the thin strips on the left and right side as well asthe area above zones C and D), B 521, C 522 (the left zone that sharesborders with zone E), D 523 (the right zone that is a mirror image ofzone C), E 524, and G 525. Zones have outer bounds and inner bounds insome embodiments, defined by the outer and inner sides of the zoneborders.

FIG. 7 illustrates a zone tree 700 for page 500, with zone P (the pageborders) a parent of zones A, C, E, and D; zone B a child of zone A; andzone G a child of zone D. Zones B, E and G are examples of islands. Anisland is a zone that does not share a border interval with its parentzone. Although zone E shares its border intervals with zones C and D,because neither of those zones actually encloses zone E, neither of themis a parent of zone E. The zone tree also illustrates that the nodeshave been populated by the content that they include. In someembodiments, the portion of a document object model (DOM) for each pageis built on the nodes of the zone tree of the page. A document objectmodel is a representation of a document as a graph whose nodes areobjects. In some embodiments, this graph is a tree, its leaf nodesrepresent primitive elements, and its non-leaf nodes are structureobjects that express the relationships between their child nodes as wellas the properties that those child nodes have as a group. In someembodiments, the order of the children of a node represents the readingorder of those children. In some embodiments, the root node is adocument node, its children are page nodes, the zone tree descends fromeach page node, a flow tree (including nodes representing structuressuch as tables, text boxes, layouts, columns, paragraphs, lists, andtext lines) descends from some of the zone nodes, and nodes representingprimitive elements (such as glyphs, shapes and images) are the childrenof some of the nodes in the flow tree. In some embodiments the structurenodes include properties that express relationships between nodes inaddition to the relationships expressed by the tree's parent-childrelationships (its directed graph edges). For example, the paragraphthat starts a new column may be a continuation of the paragraph thatends a previous column, without a paragraph break between the two. Inthis case, there would be two paragraph nodes in the tree, each with adifferent column node parent, but they would have properties pointing toone another to indicate that they are two nodes representing parts of asingle, common paragraph. A DOM, in some embodiments, is a hierarchicalrepresentation of a document that includes all the structural elementsof the document. Some embodiments define content to be a child of aparticular zone when the content is located entirely inside the outerbound of a particular zone and is not located entirely inside the outerbound of any child of the particular zone. As such, zone B includesheader text, zones C and D include standard text, and zones E and Ginclude images.

B. Rotation Groups

Some embodiments define several rotation groups on a page and analyzethe zones and content of each rotation group separately. In someembodiments, rotation groups are similar to zones except that they donot have any zone borders. Instead, a rotation group is defined toinclude all content that is rotated by the same angle (or nearly thesame angle to within a particular threshold that is sufficiently smallas to be difficult for a human viewer to distinguish). FIG. 8conceptually illustrates a process 800 of some embodiments for definingrotation groups on a page. As shown, process 800 receives (at 805) apage of a document. In some cases, the page is the only page of thedocument, whereas in other cases the page is one of multiple pages. Someembodiments perform rotation group analysis for a multi-page document(or a multi-page section) all at once, rather than page-by-page.

The process then determines (at 810) the rotation angle of each objecton a page. In some embodiments, irregularly-shaped images are assumed tohave a rotation angle of zero. For instance, the image in zone E of page500 is irregularly shaped, and would not be given a non-zero rotationangle. Horizontally-aligned text also has a rotation angle of zero,while text that is aligned off the x-axis is given a rotation angle. Forexample, the text in region F 530 of page 500 would have a rotationangle of approximately −45 degrees. Similarly, the text 610 (“Organic”and “Pure”) in page 600 would have its own rotation angle. Inembodiments that also place graphic objects into rotation groups, therectangular image 615 above text 610 would have the same rotation angleas text 610.

Next, process 800 orders (at 815) the objects by rotation angle. Theprocess then groups (at 820) the objects into clusters with a spread inrotation angle that is below a particular threshold. In someembodiments, the spread that is compared to the particular threshold isthe smallest rotation angle in the group subtracted from the largestrotation angle in the group. The use of a non-zero threshold allows thegrouping to account for minor errors in the content definition in theinitially received document (e.g., a line of text that is very slightlyoff of horizontal).

Process 800 then analyzes (at 825) each rotation group separately. Theprocess then ends. On most pages, most of the analysis will involve theupright (zero angle) group. Some embodiments do not perform zoneanalysis for groups other than the upright group, and instead simplyclassify the content of the rotated groups as children of the page as awhole. In some embodiments, each rotation group has a coordinate systemin which its content appears upright. In such embodiments, each rotationgroup has its own zone tree with content that fits into the DOM for thedocument. Some embodiments define one rotation group for eachdistinguishable angle by which content on the page is rotated. Theanalysis on each group is described in detail below.

C. Identifying Zone Borders and Intersections

FIG. 9 conceptually illustrates a process 900 of some embodiments foridentifying zone borders and intersections. Process 900 will bedescribed in conjunction with FIG. 10. FIG. 10 illustrates a page 1000that includes various graphics and text.

As shown in FIG. 9, the process receives (at 900) a rotation group andnormalizes the group to an upright coordinate system. In someembodiments, normalizing the group to an upright coordinate systeminvolves defining a coordinate system for the group such that allobjects in the group are vertical or horizontal (e.g., text lines arehorizontal in the coordinate system). The following discussion assumesthat the rotation group is the upright (zero-angle) group. One ofordinary skill in the art would be able to apply the same techniques torotation groups with non-zero angles in a coordinate system in whichtheir content appears upright. Some embodiments remove content fromother rotation groups before performing zone identification for aparticular rotation group. For instance, some embodiments would removetext 610 and image 615 from page 600 in FIG. 6 before performing zoneidentification and analysis in the upright rectilinear coordinatesystem.

The process then identifies (at 910) potential zone borders. Potentialzone borders, in some embodiments, include any horizontal or verticalgraphic object that is sufficiently narrow. The determination of whethera particular graphic object is sufficiently narrow uses an absolutemeasure (e.g., when the smaller dimension of the upright boundingrectangle of the graphic object is less than 1/24 of inch) in someembodiments. In other embodiments, the determination uses a relativemeasure (e.g., the larger dimension of the upright bounding rectangle iseight times the size of the smaller dimension), or a combination ofabsolute and relative measures (e.g., the narrow dimension could beallowed to be up to 1/12 of an inch, but the relative measure of 8:1applies). Some embodiments adjust the threshold in relation to the sizeof the page. For instance, the above examples might apply to a standard8.5×11 inch page, whereas a much larger page could have larger potentialzone borders.

Referring to FIG. 10, page 1000 includes several lines that would beclassified as potential zone borders: horizontal borders 1005-1010 andvertical borders (1011-1016). However, graphic object 1020 wouldgenerally not be considered a potential zone border, because it is toothick in the x-direction.

Some embodiments also identify all upright rectilinear shapes that haveat least a threshold size and use the sides of these shapes as potentialzone borders. In some embodiments, the threshold size is a particulararea, whereas in other embodiments a threshold width and a thresholdheight must be surpassed. For instance, object 1020 might have an arealarge enough to qualify its edges as potential zone borders, but it istoo narrow to be a separate zone. Star object 1025, on the other hand,is not an upright rectilinear shape and as such its edges would notqualify as a zone border. As such, these objects would simply beclassified as content (specifically, graphic objects) that are withinone zone or another. Some embodiments set the bounds of each potentialzone border identified as the side of an upright rectilinear shape asthe upright rectangle bounding the side, including the stroke width ifstroked. Some embodiments also include the page borders as zone bordersif they are upright rectilinear in the coordinate system of the rotationgroup.

After identifying potential zone borders, process 900 removes (at 915)borders or portions of borders that intersect with other objects on thepage. For instance, potential border 1015 is obscured by star object1025, and as such would be broken into two potential zone borders (thearea above the star and the area below the star). Some embodiments alsoremove zone borders that intersect character bounding boxes. A characterbounding box for a particular character, in some embodiments, is thesmallest rectangle that completely encloses the character. For instance,potential zone border 1010 crosses the characters “Lorem Ipsum”. Assuch, some embodiments would remove potential zone border 1010 fromconsideration.

Next, process 900 merges (at 920) borders. Some embodiments mergeborders that are parallel and either overlapping or close tooverlapping. Borders overlap when their bounds intersect. For instance,when two very narrow rectangles of different width are drawn such thatone completely envelops the other, the two potential zone borders wouldbe merged. Some embodiments slightly expand the bounds (both in widthand length of the potential zone borders to test for overlap.Accordingly, borders 1013 and 1014 in FIG. 10 would be merged into onezone border 1027, with a thickness greater than that of borders 1013 and1014.

Process 900 then determines (at 923) whether any merged borders remainunprocessed. When no borders were merged, or all merged borders havebeen processed, the process proceeds to 945, described below. Otherwise,the process selects (at 925) an unprocessed merged border. The processthen determines (at 930) whether the merged border is too thick orincludes too many zone border graphics. A merged border is too thick, insome embodiments, when its width in the narrow direction is above aparticular threshold. In some embodiments, the test for thickness is thesame as whether a graphic object is narrow enough to be classified as azone border initially. When the process determines that the border isnot too thick, the process proceeds to 923 which is described above.Otherwise, when the merged border is too thick, the process removes (at935) the merged border from the potential zone border candidates andclassifies it as a single graphic object, then proceeds to 923. Forinstance, this could happen when an image is drawn as a series of narrowrectangles or a bar graph is drawn with narrow and closely spaced bars.

Once all merged borders are examined, the process identifies (at 945)zone border intersections. As discussed above, zone border intersectionsare identified wherever a horizontal border intersects a verticalborder. Some embodiments also identify near-intersections and classifythese as intersections. To find near-intersections, borders are extendeda small amount and then tested for intersection. Some embodiments extendthe borders a fixed amount (e.g., one-fourth of an inch), while otherembodiments extend each borders an amount that is a percentage of thelength of the particular zone border. When the lengthened bordersintersect, the near-intersection is classified as an intersection andthe two borders are extended to fully cross the thickness of the other.As an example, borders 1027 and 1008 in FIG. 10 do not quite intersect.However, they are close enough that they would be classified asintersecting and are extended such that they intersect.

The process then eliminates (at 950) borders with less than twointersections. Once a border is removed, any borders that intersectedthe removed border must be retested to determine whether they still haveat least two intersections. In the example page 1000, border 1006 andthe two remaining portions of border 1015 would be removed, as they haveno zone border intersections. Once the zone borders and intersectionsare identified, the process trims (at 955) the zone borders to removeany portions extending past the outermost intersections. For instance,the borders 1027 and 1009 extend past their intersection. These would betrimmed to extend only to the outermost bound of each other. Aftertrimming the borders, the process stores (at 960) the zone border andintersection information for future use (e.g., in identifying zones).The process then ends.

At this point, the zone border intervals and zone border intersectionshave all been determined. FIG. 11 illustrates vertical zone borderintervals 1105, 1115, 1125, 1135, 1145, 1155, 1165, and 1175 as well ashorizontal zone border intervals 1110, 1120, 1130, 1140, 1150, 1160,1170, and 1180. FIG. 11 also illustrates zone border intersections 1102,1112, 1113, 1122, 1123, 1132, 1133, 1142, 1143, 1152, 1162, 1172, 1182,and 1192.

D. Identifying Zones

Once the zone borders and zone border intersections are identified, thezones can be identified. FIG. 12 conceptually illustrates a process 1200of some embodiments for identifying zones. Process 1200 will bedescribed in conjunction with FIGS. 13 and 14. FIGS. 13 and 14illustrate the application of process 1200 to identify the zones of page1000. Each of the figures is illustrated as a sequence. FIG. 13illustrates a sequence 1305-1330 to identify a first zone border. Arrowsin FIG. 13 illustrate direction vectors and dashed lines illustrate apath taken through the zone border intervals to define a zone. FIG. 14illustrates the zones identified by process 1200.

As shown in FIG. 12, process 1200 receives (at 1205) zone borders andintersections for a group or page. In some embodiments, the zone bordersand intersections are the output of process 900 described above. Theprocess then determines (at 1207) whether there are any zone borderintervals. When there are none, the process ends. Otherwise, the processassigns (at 1210) two direction vectors to each zone border interval(i.e., horizontal intervals have vectors pointing right and left, andvertical intervals have vectors pointing up and down). FIG. 13illustrates (at 1305) that each of the border intervals for page 1000starts with direction vectors in both directions.

Next, the process selects (at 1215) a border interval b, an intersectioni, and a direction d. Some embodiments select the starting pointrandomly, whereas other embodiments use a heuristic such as the top- andleft-most intersection in a particular direction. 1305 illustrates arandom selection of starting at intersection 1182 moving upwards alonginterval 1115. Process 1200 then proceeds (at 1220) in the direction dfrom intersection i until arriving at the next intersection.

Once the intersection is reached, the process determines (at 1225)whether the intersection is the starting intersection selected at 1215.When the intersection is the original starting intersection, the processproceeds to 1265 which is described below. Otherwise, the processdetermines (at 1230) whether the path through the zone border intervalscan turn clockwise at the intersection. When the path can turnclockwise, the path does so (at 1235). The process then proceeds to 1255which is described below. When the path cannot turn clockwise, theprocess determines (at 1240) whether the path can continue straightthrough the intersection. When the path can continue straight, then thepath does so (at 1245). The process then proceeds to 1255 which isdescribed below. When the path cannot continue straight, the path turns(at 1250) counterclockwise to the next border interval. By the choicesmade in steps 1230 and 1240, the process 1200 exhibits a preference fora clockwise turn at each border intersection. Some embodiments willinstead exhibit a preference for counterclockwise turns, which gives thesame results.

The process sets (at 1255) the new border interval as the current borderinterval b, and the new intersection as the current intersection i. Theprocess then sets (at 1260) the direction d moving away fromintersection i along border b. The process then proceeds to 1220 whichwas described above.

Once the original intersection is reached, process 1200 defines (at1265) a zone Z as the set of border intervals traversed since operation1215. As noted above, FIG. 13 illustrates the traversal of a set of zoneborder intervals according to process 1200. At 1305, after selectinginterval 1145 moving up from intersection 1182 to start (shown by thecircle and short arrow in the figure), the path comes to intersection1112. Turning clockwise is an option, so the path turns (at 1310) tointerval 1120, then clockwise again at intersection 1122 to interval1155. The path turns (at 1315) clockwise yet again at intersection 1132to interval 1150, but then at intersection 1142 cannot either turnclockwise or continue straight through. Instead, the path turnscounterclockwise to interval 1145, then again at intersection 1152 tointerval 1160 to proceed towards intersection 1162. At intersection1162, the path turns (at 1320) clockwise to interval 1175, thenclockwise again at intersection 1172 to interval 1180. Interval 1180returns to the path to the original intersection 1182.

FIG. 13 illustrates (at 1325) the zone 1300 defined by the traversal ofintervals 1115, 1120, 1155, 1150, 1145, 1160, 1175, and 1180, as well asthe direction vectors used in that traversal. Returning to process 1200,after defining (at 1265) the zone Z, the process removes (at 1270) thedirection vectors used to traverse zone Z. FIG. 13 illustrates (at 1330)the zone border intervals of page 1000 with the direction vectors usedto traverse zone 1300 removed.

Process 1200 next removes (at 1275) all border intervals with noremaining direction vectors. This will not occur after the first zone isidentified, but can happen after any of the further zones areidentified. When the zone Z is an island (i.e., a zone that shares noborders with its parent), then the process 1200 classifies (at 1280) thezone as such. In embodiments in which the preference is for clockwiseturns, then a zone defined by traversing its center in acounterclockwise direction will be an island.

The process then determines (at 1285) whether any zone border intervalsremain. When more zone border intervals remain, the process proceeds to1215 which was described above. Otherwise, once all zone borderintervals are used in both directions, the process has defined all thezones for the page. The process then stores (at 1290) the zoneinformation. The process then ends.

FIG. 14 illustrates the continuation of the process 1200 applied to page1000. For simplicity, FIG. 14 does not illustrate every move through thetraversal of the zone border intervals. First, starting at any of theintersections 1113, 1123, 1133, and 1143, the two zones 1435 and 1440are identified. These zones are duplicates of each other, as will occurin the case of islands that have no non-island children. Someembodiments remove duplicate zones. Other embodiments, however, treatthe zones as two: one that is a regular zone, and the other that is anisland. Next, starting at intersection 1192 results in zone 1445 (thepage borders), because all possible turns off of the page borders wouldbe counterclockwise moves. Finally, this leaves zones 1450 and 1455,which are traversed and removed. Once all the zones are traversed, thereare no remaining zone border intervals.

E. Generating the Zone Tree

Once the zones have been identified, the zone graph (zone tree) can begenerated. The zone tree is used, in some embodiments, in documentreconstruction that is done on a zone-by-zone basis. FIG. 15conceptually illustrates a process 1500 of some embodiments forgenerating a zone tree. As shown, the process receives (at 1505) zonesand content objects. In some embodiments, these zones have beenidentified by a process such as process 1200. The process then sorts (at1510) the zones by area. Some embodiments treat an island as larger thana non-island when their areas are equal for the purposes of sorting thezones.

Next, the process selects (at 1515) the smallest zone as z. The processthen determines (at 1520) whether zone z has a node yet in the zonegraph for the page. When z has a node, the process proceeds to 1530which is described below. Otherwise, when z does not yet have a node,the process 1500 defines (at 1525) a node for zone z.

Next, the process selects (at 1530) the next smallest zone as zone p.The process then determines (at 1535) whether zone p contains zone z(i.e., whether the outer bounds of zone z are completely within theouter bounds of zone p). When zone p contains zone z, the processdetermines (at 1540) that zone z is a child of zone p. Based on this,the process defines (at 1545) a node for zone p in the node graph. Theprocess then defines (at 1550) an edge from zone p to zone z. Theprocess then proceeds to 1565 which is described below.

When, at 1535, the process determines that zone p does not contain zonez, the process determines (at 1555) whether there are any zones largerthan the current zone p. When there are larger zones remaining, theprocess proceeds to 1530 and selects the next smallest zone as zone p totest whether the new zone p is a parent of zone z. Otherwise, when thereare no zones larger than zone p, the process determines (at 1560) thatzone z has no parent zones.

Next, the process determines (at 1565) whether there are any zoneslarger than zone z. When there are larger zones, the process removes (at1570) zone z from the set of zones from which to select and proceeds to1515 to select another zone for parent-child analysis.

FIG. 16 illustrates the zones 1435 (A), 1440 (A′), 1455 (B), 1450 (C),1300 (D) and 1445 (E) of page 1000 (shown in FIG. 10) sorted in sizeorder (A′ is the island for A) and placed into node graph 1600. Usingprocess 1500, first a node for zone A (the smallest zone) would bedefined, then the zones would be tested until the process determinedthat island zone A′ was a parent of zone A, at which point zone A wouldbe defined in the node graph, and an edge from A′ to A would be defined.Next, zone D would be determined to be the parent of island zone A′, andthen zones B, C, and D would all be determined to be children of islandzone E, which has no parents. In some embodiments, levels of zones andisland zones always alternate in the zone graph. Thus, islands E and A′are at the first and third level of graph 1600, and zones B, C, D, and Aare at the second and fourth level.

Once all zones have been analyzed, the process proceeds to 1573 anddetermines whether there are any unprocessed content objects. When thereare no content objects (i.e., the document is blank except for zoneborders), or all content objects have been processed, the processproceeds to 1597, described below. Otherwise, the process proceeds to1575 and selects a content object c. The process then defines (at 1580)a node for the object c. A content object, in some embodiments, is aprimitive object (e.g., a glyph, shape or image). The process thendetermines (at 1585) the smallest zone x that contains content object c.Once the zone x containing content object c is determined, the processdefines (at 1590) an edge in the zone graph from zone x to contentobject c. When all objects have been added, the process stores (at 1597)the zone graph. The process then ends.

In some embodiments, the content in each zone is further analyzed (e.g.,grouping text into paragraphs, identifying tables, etc.). Furthermore,as document reconstruction is performed, some embodiments update thezone graph with content subtrees for each zone, where those contentsubtrees include structure nodes that represent the hierarchicalgrouping of the primitive objects of the zone. By performing zoneanalysis first, one ensures that content from different zones is notinappropriately grouped in the subsequent document reconstruction steps.

In some embodiments, the identification of geometric attributes such asboundaries and the regions bordered by those boundaries (e.g., zones)sets the stage for further document reconstruction. For example,profiles may depend on zone geometry and structure elements such astables or text boxes may be recognized from the zone geometry.

F. Software Architecture

In some embodiments, the zone analysis processes described above areimplemented as software running on a particular machine, such as acomputer, a media player, a cell phone (e.g., an iPhone®), or otherhandheld or resource-limited devices (or stored in a computer readablemedium). FIG. 17 conceptually illustrates the software architecture of azone analysis application 1700 of some embodiments for performing zoneanalysis on a document. In some embodiments, the application is astand-alone application or is integrated into another application (e.g.,a document reconstruction application), while in other embodiments theapplication might be implemented within an operating system.

Zone analysis application 1700 includes a border identification module1705, an interval and intersection identification module 1710, a zoneidentification module 1715, and a zone graph builder 1720, as well aszone information storage 1725.

FIG. 17 also illustrates document content 1730. Border identificationmodule 1705 receives information from the document content 1730. In someembodiments, this information is information about all of the graphics(e.g., shapes, images, lines, etc.) in the document. The borderidentification module 1705 identifies potential zone borders and passesthis information to the interval and intersection identification module1710, as well as to the zone information storage 1725. In someembodiments, border identification module 1705 performs some or all ofprocess 900.

The interval and intersection identification module 1710 receives zoneborder information from the border identification module 1705 and/or thezone information storage 1725. The interval and intersectionidentification module 1710 identifies zone border intersections and zoneborder intervals based on the potential zone borders identified bymodule 1705. The identified zone border intersections and zone borderintervals are passed to the zone identification module 1715 as well asstoring in zone information storage 1725. In some embodiments, intervaland intersection module identification 1710 performs some or all ofprocess 900.

The zone identification module 1715 receives zone border informationfrom the border identification module 1705, zone border intersection andzone border interval information from the interval and intersectionidentification module 1710, and/or information from the zone informationstorage 1725. Zone identification module 1715 identifies zones based onthe information from modules 1705 and 1715. The identified zones arepassed to the zone graph builder as well as storing in the zoneinformation storage 1725. In some embodiments, zone identificationmodule 1715 performs some or all of process 1200.

The zone graph builder 1720 module receives zone information from thezone identification module 1715 and/or the zone information storage1725, as well as content information from the document content 1730.Zone graph builder 1720 defines the zone graph for a document based onthe zone information, and populates the zone graph with contentinformation. In some embodiments, the zone graph builder 1720 populatesthe zone graph as content information is identified by otherreconstruction processes, such as those described in the Sections below.In some embodiments, zone graph builder 1720 performs some or all ofprocess 1500.

In some embodiments, the results of the processes performed by theabove-described modules or other modules are stored in an electronicstorage (e.g., as part of a document object model). The document objectmodel can then be used for displaying the document on an electronicdisplay device (e.g., a handheld device, computer screen, etc.) suchthat a user can review and/or interact with the document (e.g., viatouchscreen, cursor control device, etc.).

III. Guide and Gutter

Some embodiments of the invention provide methods for identifyinggeometric attributes such as boundaries (e.g., alignment guides) andunfilled space (e.g., gaps of unfilled white space between groups ofglyphs, called gutters) in a document or portion of a document. In someembodiments, a gutter is the white space between two alignment points(e.g., between a right-alignment point and a left-alignment point).Identification of guides and gutters is used in subsequentreconstruction procedures, such as column identification and splittingof text lines, in some embodiments. Some embodiments identify guides andgutters on a zone-by-zone or page-by-page basis.

FIG. 18 illustrates an overall process 1800 of some embodiments foridentifying guides and gutters in a document. Process 1800 will bedescribed in conjunction with FIG. 19, which illustrates a page 1900having two columns of text, and the guides and gutters identified onpage 1900. As shown in FIG. 18, process 1800 receives (at 1805) aportion of a document. This portion may be multiple pages, a page, or azone that has been identified by prior zone analysis. The portion ofdocument may include words that have been reconstructed from glyphprimitives by methods described elsewhere in this application.

The process then applies (at 1810) cluster analysis to determine guidesof the received document portion. Cluster analysis enables the processto determine x-coordinates where the ends or beginnings of words aregrouped together, making those x-coordinates likely alignment guides. Asmentioned, FIG. 19 illustrates a page 1900 with two columns of text.Page 1900 includes as set of guides 1905. Some embodiments determinebottom and top lines of columns as guides, whereas other embodimentsonly determine left- and right-alignment guides. Some embodiments alsoidentify guides for other alignments, such as center alignment or thealignment of decimal points in listings of numbers. Cluster analysis andthe guide determination process are described in further detail below.

Next, the process determines (at 1815) the gutters of the documentportion. Some embodiments use information from operation 1810 todetermine the gutters. FIG. 19 illustrates a gutter 1910 that isdetermined for page 1900 between the right-alignment guide of column oneand the left-alignment guide of column two. Some embodiments treat thepage margins as gutters, while other embodiments do not. Once the guidesand gutters are determined, the process 1800 uses (at 1820) the guidesand gutters for further reconstruction of the document. The process thenends.

A. Density Clustering

Some embodiments determine right- and left-alignment guides by searchingfor text lines that start or end at the same or nearly the samex-coordinate on a page and determining whether sufficient evidenceexists that the x-coordinate is actually an alignment point. Someembodiments use a form of cluster analysis called density clustering todetermine alignment guides. The density clustering of some embodimentstakes advantage of the memory and processing efficiencies describedbelow in Section VI so that it can be performed on a resource-limiteddevice (e.g., an iPhone®).

Density clustering is often applicable to problems in which there is asubstantial amount of “noise” or random data mixed in with otherwiseclearly visible clusters. When the data is a set of real numbers, theclusters are identified as subsets that optimally meet given densityconstraints. The constraints are generally designed to pick out subsetsthat are relatively denser than others. For instance, some embodimentsuse a minimum size of a cluster and a maximum spread of a cluster asconstraints.

FIG. 20 conceptually illustrates a process 2000 of some embodiments forperforming density clustering. As shown, the process receives (at 2005)a set of input data. In some embodiments, the input data is coordinatedata of character glyphs on a page. For example, in using densityclustering to find left-alignment guides, the input data is thex-coordinate of the anchor of the first letter of each word on the page.

The process then sorts (at 2010) the set of input data. Some embodimentssort the data in ascending order, while other embodiments sort the datain descending order. For instance, in the case of using densityclustering to determine alignment guides, the data (x-coordinate values)is sorted from lowest to highest x-coordinate value such that if twox-coordinate values are equal they are next to each other in the sorteddata (unless there are other words with the same x-coordinate value thatfall in-between the two). Some embodiments create a new array for thesorted data, while some embodiments use an indirectly sorted array ofindices as described below in Section VI.

Next, process 2000 determines (at 2012) whether the set has at least twopieces of data. If not, then the process ends, as there is nothing tocluster. Otherwise, the process proceeds to determine (at 2015) the setof differences between subsequent data in the sorted set. Such a setwill have one less value than the set of input data. As an example, whenthere are three words on a page, the two values in the set ofdifferences are the difference between the x-coordinate values of thefirst and second words and the difference between the x-coordinatevalues of the second and third words.

Next, the process sets (at 2020) a variable d to the largest unevaluateddifference in the set of differences. For instance, when the differencesfor a set of words are 0.7 inches, 0.2 inches, 0.0 inches, and 0.4inches, then the variable d would initially be set to 0.7 inches. Theprocess then partitions (at 2025) the sorted data wherever thedifference is greater than or equal to d to generate a set of subsets ofthe data. The first partition will always partition the sorted data onlyat differences equal to d, because d will be set to the largestdifference. In the above example of five data values with differences of0.7, 0.2, 0.0, and 0.4, the partitioning would generate two subsets (thefirst value in one subset and the other four in the other subset).

The process then determines (at 2030) the set S of subsets that satisfyparticular constraints for the problem being solved. In someembodiments, the purpose of the constraints is to determine subsets thatare relatively denser than the other subsets. Some embodiments use twodensity constraints: a minimum cluster size (i.e., the minimum number ofvalues in the subset) and maximum cluster spread (i.e., the largestallowed difference between the largest and smallest values in thesubset). In the case of using density clustering for determiningalignment guides, some embodiments use a minimum cluster size that is afraction of the total lines in the page or zone being evaluated, whileother embodiments use a constant. Some embodiments use a maximum spreadthat is a fraction of the median font size of the first (forleft-alignment) or last (for right-alignment) characters of words.

Once the set S of subsets that satisfy the constraints are determined,the process determines (at 2035) whether S is empty. When S is empty,the process proceeds to 2055 which is described below. When S includesat least one subset, the process evaluates (at 2040) an optimizationfunction for S. Some embodiments use an optimization function that looksfor the set S that has the largest subset that meets the constraints.Other embodiments use an optimization function tries to maximize the sumof the squares of a particular value (e.g., the size of the subset minusthe minimum cluster size) over all of the subsets that meet theconstraints. Yet other embodiments use one of the above-mentionedoptimization functions, and then use the other in case of a tie. Otheroptimization functions are used by other embodiments.

Next, the process determines (at 2045) whether the set S is the mostoptimal so far, based on the optimization function. When S is not themost optimal, the process proceeds to 2055 which is described below.Otherwise, when S is the most optimal, the process stores (at 2050) S asthe best set of clusters yet found. The first pass through (in which dis the largest difference) will always be the most optimal at thatpoint, if S is not empty. On subsequent passes, the current S will becompared to the stored set of clusters.

The process then determines (at 2055) whether there are any unevaluateddifferences. Some embodiments test each possible partition to find themost optimal set of clusters. Some such embodiments use the efficiencytechniques described below in Section X to enable faster and moreefficient processing. When the process determines that there areunevaluated differences, the process proceeds to 2020 which wasdescribed above.

Otherwise, once all the differences have been evaluated, the processoutputs (at 2060) the currently stored optimal set (or empty set if noclusters satisfying the constraints were found) as the final set ofclusters. In the case of determining alignment guides, the final set ofclusters would be groups of words with very close x-coordinates. Theprocess then ends. One of ordinary skill will recognize that in additionto the density constraints and optimal measure, process 2000 imposes aconsistency constraint on the clusters; namely, that intra-clusterdifferences between successive values in a cluster will never equal orexceed inter-cluster differences, because the data is always partitionedat all differences that are equal to or greater than a specified gapminimum.

B. Determining Alignment Guides

As mentioned above, some embodiments determine right- and left-alignmentguides by searching for associated sets of glyphs (e.g., words, textlines) that start or end at the same or nearly the same x-coordinate ona page and determining whether sufficient evidence exists that thex-coordinate is actually an alignment point. Some embodiments usesimilar but not identical processes to find left-alignment guides andright-alignment guides.

FIG. 21 conceptually illustrates a process 2100 of some embodiments fordetermining left-alignment guides. Portions of process 2100 will bedescribed in conjunction with FIGS. 22-24. FIGS. 22-24 illustrate theprocess of identifying a left-alignment guide on a page 2200. As shownin FIG. 21, process 2100 sets (at 2105) the input data for densityclustering as the x-coordinates of the left edge of words in a region ofa document. The region is a page or a zone of a page in someembodiments. In some embodiments, the left edge of a particular word isthe x-coordinate of the anchor of the first glyph in the particularword, adjusted to the left alignment position expected for the glyph.

The process then determines (at 2110) desired cluster properties. Insome embodiments, the cluster properties are the constraints for densityclustering described above. Some embodiments use two densityconstraints: a minimum cluster size (i.e., the minimum number of valuesin the subset) and maximum cluster spread (i.e., the largest alloweddifference between the largest and smallest values in the subset). Inthe case of using density clustering for determining alignment guides,some embodiments use a minimum cluster size that is a fraction of thetotal lines in the page or zone being evaluated, while other embodimentsuse a constant. Some embodiments use a maximum spread that is a fractionof the median font size of the first (for left-alignment) or last (forright-alignment) characters of words. One example of constraints arethat the minimum cluster size is 5% of the total number of text lines inthe region, and the maximum spread is 10% of the median font size.

Next, the process applies (at 2115) density clustering to the input datausing the determined cluster properties to determine clusters ofx-coordinate values that may be alignment guides. Some embodiments useprocess 2000 as described above.

Process 2100 then determines (at 2117) whether there are any unevaluatedclusters. When there are no clusters, or all clusters are evaluated, theprocess ends. Otherwise, the process selects (at 2120) a cluster (i.e.,one of the clusters output from the cluster analysis). The process thensets (at 2125) a left-alignment guide as a rectangle with the minimumand maximum x-coordinates as the smallest and largest values in thecluster and the minimum and maximum y-coordinates as the top and bottomof the page. In some cases, the minimum and maximum x-coordinate will bethe same, as all the x-coordinates in the cluster will have the samevalue. In other cases, small aberrations or words that accidentally makeit into the cluster will give the rectangle a non-zero width.

FIG. 22 illustrates a page 2200 with a potential left-alignment guide2205 in some embodiments. The minimum x-coordinate of the rectangle 2205is set by the left edge of the right column 2215, while the maximumx-coordinate is set by the word “tate” 2210 in the middle of the page,because the start of word 2210 is close enough to the start of the wordsforming the left edge of the right column that it is grouped in withthose words by the density clustering process.

Process 2100 then removes (at 2130) the rectangle at y-coordinates thatdo not satisfy constraints based on an analysis of words that start inthe rectangle and words that cross the rectangle. The process thenproceeds to 2117, described above. Some embodiments remove a portion ofthe rectangle anywhere that a word starts left of the rectangle andcrosses into the rectangle. The rectangle is also removed at anyy-coordinate that is between two crossing words that do not have asufficient number of border words between them. A border word is a wordthat starts in or at one of the edges of the rectangle. Some embodimentsuse a requirement that there be at least five border words betweencrossing words, and at least one of those five border words must be theleftmost on its text line or separated from the previous word on itstext line by more than a normal word gap. Some embodiments use processesdescribed in U.S. Publication No. 2007/0250497, entitled “SemanticReconstruction”, by Mansfield, et al., which is incorporated herein byreference, to determine word gaps and larger gaps. Some embodiments usedifferent requirements (e.g., fewer or greater than five border wordsbetween crossing words) to perform operation 2130.

FIG. 23 illustrates the page 2200 and rectangle 2205 with the crossingwords for rectangle 2205 circled. The crossing words include words 2340(“reprehenderit”) and 2315 (“dolore”), among others. There are twoborder words 2210 (“tate”) and 2325 (“esse”) between crossing words 2340and 2315; however, when the requirement for border words in betweencrossing words is three or larger, the rectangle would be removedthrough this section as well. Some embodiments remove only from thegreatest ascent to the greatest descent of crossing words andnon-qualifying areas in between crossing words. Other embodiments alsoremove areas that are likely beyond the alignment guides, such as thearea from the crossing word 2330 (“auteir”) to the border word 2335(“reprehenderit”) above it.

FIG. 24 illustrates left-alignment guides 2405 and 2410 for page 2200.Because of the call-out region in the center of the page, theleft-alignment guides at that particular x-coordinate do not run thelength of the entire page 2200.

As mentioned above, some embodiments use a process similar to process2100 for determining right-alignment guides. FIG. 25 conceptuallyillustrates a process 2500 of some embodiments for determiningright-alignment guides. As shown, the process sets (at 2505) the inputdata for density clustering as the x-coordinates of the right edge ofwords in a region of a document. The region is a page or a zone of apage in some embodiments. In some embodiments, the right edge of aparticular word is the x-coordinate of the anchor of the last glyph inthe particular word plus the x-coordinate of the advance vector for thelast glyph in the word, adjusted to the right alignment positionexpected for the glyph.

The process then determines (at 2510) desired cluster properties. Insome embodiments, the cluster properties are the constraints for densityclustering described above. Some embodiments use two densityconstraints: a minimum cluster size (i.e., the minimum number of valuesin the subset) and maximum cluster spread (i.e., the largest alloweddifference between the largest and smallest values in the subset). Inthe case of using density clustering for determining alignment guides,some embodiments use a minimum cluster size that is a fraction of thetotal lines in the page or zone being evaluated, while other embodimentsuse a constant. Some embodiments use a maximum spread that is a fractionof the median font size of the first (for left-alignment) or last (forright-alignment) characters of words. One example of constraints arethat the minimum cluster size is 5% of the total number of text lines inthe region, and the maximum spread is 10% of the median font size.

Next, the process applies (at 2515) density clustering to the input datausing the determined cluster properties to determine clusters ofx-coordinate values that may be alignment guides. Some embodiments useprocess 2000 as described above.

The process then determines (at 2517) whether there are any unprocessedclusters. When there are no clusters, or all clusters have beenprocessed, the process ends. Otherwise, the process selects (at 2520) acluster (i.e., one of the clusters output from the cluster analysis).The process then sets (at 2525) a right-alignment guide as a rectanglewith the minimum and maximum x-coordinates as the smallest and largestvalues in the cluster and the minimum and maximum y-coordinates as thetop and bottom of the page. In some cases, the minimum and maximumx-coordinate will be the same, as all the x-coordinates in the clusterwill have the same value. In other cases, small aberrations or wordsthat accidentally make it into the cluster will give the rectangle anon-zero width.

The process then removes (at 2530) the rectangle at y-coordinates thatdo not satisfy constraints based on an analysis of words that end in therectangle and words that cross the rectangle. The process then proceedsto 2517, described above. Some embodiments remove a portion of therectangle anywhere that a word crosses or starts in the rectangle andends right of the rectangle. The rectangle is also removed at anyy-coordinate that is between two crossing words that do not have asufficient number of border words between them. A border word is a wordthat ends in or at one of the edges of the rectangle. Some embodimentsuse a requirement that there be at least five border words betweencrossing words, and at least one of those five border words must be therightmost on its text line or separated from the next word on its textline by more than a normal word gap. Some embodiments use processesdescribed in United States Publication No. 2007/0250497 to determineword gaps and larger gaps. Some embodiments use different requirements(e.g., fewer or greater than five border words between crossing words)to perform operation 2530.

C. Determining Gutters

After determining the guides, some embodiments then determine gutters ofthe region (e.g., zone, page, etc.). Some embodiments use informationfrom the guide determination process (e.g., processes 2100 and 2500) todetermine the groupings of unfilled white space between associatedglyphs (e.g., gutters) of the region. Some embodiments also use otheralignment points in addition to guides for determining gutters in aregion.

FIG. 26 conceptually illustrates a process 2600 of some embodiments fordetermining gutters for a region. Portions of process 2600 will bedescribed in conjunction with FIGS. 27-29. FIGS. 27-29 illustrate theprocess of identifying a gutter on a page 2700.

As shown in FIG. 26, the process receives (at 2605) alignmentinformation. In some embodiments, this information is the guidesdetermined by processes 2100 and 2500. Some embodiments include otheralignment points as well as guides. For instance, in some embodiments,the end of text lines in left-aligned (not justified) text are treatedas right-alignment points. This enables gutters to be identified incolumn gaps even if no guide is found at the right edge of the firstcolumn. Similarly, the left edge of right-aligned text, or both edges ofcentered text, are considered alignment points in some embodiments.

Process 2600 then determines (at 2607) whether there are any unprocessedright-alignment points. When there are no right alignment points, or allhave been processed, the process ends. Otherwise, the process selects(at 2610) a right-alignment point. In some embodiments, the processidentifies the leftmost right-alignment point first, while in otherembodiments it picks a random right-alignment point.

The process then determines (at 2615) whether a left-alignment pointexists between the selected right-alignment point and the right edge ofthe region. When there are no left-alignment points, the processproceeds to 2607, which was described above. Otherwise, when there is atleast one left-alignment point between the right-alignment point and theregion edge, the process identifies (at 2620) the next left-alignmentpoint moving right across the region from the selected right-alignmentpoint. It is the area between these two points that the process tests todetermine if there is a gutter.

Once the right- and left-alignment points are identified, the processsets (at 2625) a gutter as a rectangle with the right-alignment point asthe minimum x-coordinate and the left-alignment point as the maximumx-coordinate. The minimum and maximum y-coordinates of the rectangle arethe top and bottom of the page. FIG. 27 illustrates the page 2700 and arectangle 2705 that is to be tested as a possible gutter. The minimumx-coordinate is the right-alignment point at the right edge of the firstcolumn, and the maximum x-coordinate is the left-alignment point at theleft edge of the second column.

Next, the process removes (at 2630) the gutter at y-coordinates that donot satisfy constraints based on an analysis of words that cross intothe rectangle and border the rectangle. Some embodiments remove aportion of the rectangle anywhere that a word crosses into or starts inthe rectangle. The rectangle is also removed at any y-coordinate that isbetween two crossing words that do not have a sufficient number ofborder words between them. A border word for a gutter is a word thatends at the left edge of the rectangle or starts at the right edge ofthe rectangle. Some embodiments use a requirement that there be at leastfive border words between crossing words, and at least one of those fiveborder words must be either the leftmost on its text line or separatedfrom the previous word on its text line by more than a normal word gapor the rightmost on its text line or separated from the next word on itstext line by more than a normal word gap. Some embodiments use processesdescribed in the above mentioned U. S. Publication No. 2007/0250497, todetermine word gaps and larger gaps. Some embodiments use differentrequirements (e.g., fewer or greater than five border words betweencrossing words) to perform operation 2630. The process then proceeds to2607, which was described above.

FIG. 28 illustrates the page 2700 and rectangle 2705 with the crossingwords for rectangle 2705 circled. The crossing words include words 2810(“cillum”) and 2815 (“nulla”), among others. There is a border word 2820(“eu”) between crossing words 2810 and 2815; however, if the requirementfor border words in between crossing words is two or larger, then therectangle would be removed through this section as well. Someembodiments remove only from the greatest ascent to the greatest descentof crossing words and non-qualifying areas in between crossing words.Other embodiments also remove areas that are likely beyond the gutters.

FIG. 29 illustrates gutters 2905 and 2910 for page 2700. Because of thecall-out region in the center of the page, the gutter between the twomain columns does not run the entire length of the page.

Some embodiments use the guides and gutters throughout the semanticreconstruction process. For example, gutters are used to split textlines and identify columns, processes that are described below inSection IV.

D. Software Architecture

In some embodiments, the guide and gutter analysis processes describedabove are implemented as software running on a particular machine, suchas a computer, a media player, a cell phone (e.g., an iPhone®), or otherhandheld or resource-limited devices (or stored in a computer readablemedium). FIG. 30 conceptually illustrates the software architecture of aguide and gutter analysis application 3000 of some embodiments foridentifying guides and gutters in a document. In some embodiments, theapplication is a stand-alone application or is integrated into anotherapplication (e.g., a document reconstruction application), while inother embodiments the application might be implemented within anoperating system.

Guide and gutter analysis application 3000 includes a guideidentification module 3005, a density clustering module 3010, and agutter identification module 3015, as well as guide and gutterinformation storage 3020.

FIG. 30 also illustrates document content 3025. Guide identificationmodule 3005 receives information from the document content 3025. Theguide identification module 3005 analyzes the document content toidentify alignment guides in the document. The identified guides arepassed to gutter identification module 3015 as well as to guide andgutter information storage 3020 and to the document content 3025. Insome embodiments, guide identification module 3005 performs some or allof processes 2100 and 2500.

The guide identification module 3005 also passes information to, andreceives information from, the density clustering module 3010. Densityclustering module 3010 receives input data from the guide identificationmodule 3005 and/or the guide and gutter information storage 3025 andperforms density clustering on the input data in order to determinepotential guides. In some embodiments, density clustering module 3010performs some or all of process 2000.

The gutter identification module 3015 receives information from theguide identification module 3005 and the document content 3025. Thegutter identification module analyzes the received information toidentify gutters in the document. The identified gutters are passed tothe guide and gutter information storage 3020 and to the documentcontent 3025. In some embodiments, gutter identification module 3015performs some or all of process 2600.

In some embodiments, the results of the processes performed by theabove-described modules or other modules are stored in an electronicstorage (e.g., as part of a document object model). The document objectmodel can then be used for displaying the document on an electronicdisplay device (e.g., a handheld device, computer screen, etc.) suchthat a user can review and/or interact with the document (e.g., viatouchscreen, cursor control device, etc.).

IV. Determining the Layout and Flow

Documents generally have an implicit structure and flow of content.Specifically, in some cases, ordered sequences of characters (and inlinegraphics) make up words, ordered sequences of words make up text lines(or span text lines with a hyphen), ordered sequences of text lines makeup paragraphs, ordered sequences of paragraphs make up columns (or spancolumns), ordered sequences of columns make up layouts, and orderedsequences of layouts make up sections of a document. When this structureis not provided in the file format of an electronic document, thestructure has previously been inaccessible to software. While merelyviewing a document does not necessarily require document structure,applications for editing, importing, searching, styling, or otherwiserepurposing a document do require knowledge of the document structureand flow in order to function properly.

Some embodiments of the invention provide methods for determining thelayout and flow of a document or a region of a document. This includesdetermining the semantic hierarchy (e.g., the words, lines, andparagraphs of a document), as well as layout properties such as thecolumns and how the columns fit together for intended reading of thedocument. In some embodiments, the goal of the processes is to identifythe order in which a human would read a document from start to finish.

FIG. 31 conceptually illustrates a process 3100 of some embodiments fordetermining the layout and flow of a document. Process 3100 will bedescribed in conjunction with FIG. 32. FIG. 32 illustrates a sequence ofvarious layout and flow information being determined for a page 3200 ofa document with two columns of text. In FIG. 32, one will recognize thatthe content of page 3200 is not important, but rather that the lines,paragraphs, etc. are of import. As shown in FIG. 31, process 3100receives (at 3105) a portion of a document. In some embodiments, theportion is the entire document, or a section, page, or zone.

The process then identifies (at 3110) lines of text in the receiveddocument. This includes identifying characters that share a commonbaseline and merging preliminary lines together when necessary (e.g.,subscripts and superscripts). FIG. 32 illustrates the identification oflines 3205 and 3210. The line identification process of some embodimentsis described in further detail below in subsection A.

Next, the process identifies (at 3115) words in the text. Someembodiments use difference clustering, as described in above mentionedU. S. Publication No. 2007/0250497 to identify words in the text. FIG.32 illustrates the identification of words on page 3200, including theword 3215 (“Lorem”) from line 3205 and the word 3220 (“amet”) from line3210. The word identification process is also described in furtherdetail below in subsection B

The process then splits (at 3120) the lines of text where the text isdiscontinuous. FIG. 32 illustrates that line 3205 is split into lines3225 and 3230, and line 3210 is split into lines 3235 and 3240. The linesplitting process of some embodiments is described in further detailbelow in subsection C.

After splitting the lines, the process places (at 3125) the text linesinto paragraphs. FIG. 32 illustrates paragraphs 3245 and 3250 identifiedon page 3200. The paragraph identification process is described infurther detail below in subsection D.

Lastly, the process places (at 3130) the paragraphs into columns andlayouts. FIG. 32 illustrates columns 3255 and 3260 identified on page3200. The column and layout identification process is described infurther detail below in subsection E.

Some embodiments do not perform all of the operations of process 3100 atonce. Instead, some perform other document reconstruction processes inbetween operations of process 3100. For example, some embodimentsdetermine lines of text and the words in the text, but then identifyguides and gutters prior to splitting the lines of text.

A. Initial Line Identification

As mentioned above, in some embodiments lines of text have to beidentified. Because every character in a particular line of text willnot necessarily always share a common baseline, some embodiments attemptto merge lines together based on evidence that the characters in the twolines are intended to be read as part of the same line of text (e.g.,superscripts and subscripts).

FIG. 33 conceptually illustrates a process 3300 of some embodiments foridentifying and merging lines of text. Process 3300 will be described inconjunction with FIGS. 34 and 35. FIG. 34 illustrates a page 3400 withsix groups 3405-3430 of overlapping text lines, and FIG. 35 illustratesthe merging of those groups of text lines according to some embodimentsof the invention.

As shown in FIG. 33, the process receives (at 3305) a portion of adocument. In some embodiments, the portion is a page of a document, or azone of a page, etc. The process then determines (at 3307) whether thereare any characters in the document portion. When there are none, theprocess ends. Otherwise, the process associates (at 3310) as preliminarytext lines characters that share a common baseline. Characters share acommon baseline in some embodiments when they have the same y-coordinateanchor point. In general, associating characters that share a commonbaseline will group together lines of standard text. Some embodimentsuse a small threshold such that the y-coordinate anchor points in apreliminary text line need not be exactly equal, but must be within thesmall threshold of each other.

Next, the process identifies (at 3315) groups of text lines thatvertically overlap. Two lines vertically overlap in some embodimentswhen the bounding rectangle of the first line overlaps in y-coordinatevalues with the bounding rectangle of the second line. FIG. 35illustrates the page 3400 with six groups of vertically overlapping textlines: lines 3505 and 3506, lines 3510 and 3511, lines 3515 and 3516,lines 3520, 3521, and 3522, lines 3525 and 3526, and lines 3530 and3531. Line 3520 is associated in a group with line 3522 because bothoverlap with line 3521, even though they do not overlap each other. Eventhough there is no horizontal overlap, because lines 3530 and 3531vertically overlap, they are initially grouped together in someembodiments.

The process then selects (at 3320) an unevaluated group and partitions(at 3325) the group into sections with no horizontal overlap betweentext lines of different sections. Two text lines horizontally overlap insome embodiments when the x-coordinates of the bounding box of the firsttext line overlap with the x-coordinates of the bounding box of thesecond text line. For instance, lines 3530 and 3531 are partitioned atthis point because they do not horizontally overlap and thus would notbe likely to be considered the same line. Some embodiments expand themeasure of horizontal overlap a small distance (e.g., one half of aspace character) at the beginning and end of the text lines, so thatoffset characters (e.g., subscripts and superscripts) at the beginningor end of a line are merged. For example, there is no horizontal overlapbetween lines 3510 and 3511, but they are not partitioned because theend of line 3510 is close enough to the beginning of line 3511.

After partitioning the selected group, the process selects (at 3330) anunevaluated section from the group and sorts (at 3335) the lines in thesection from top to bottom. Thus, if the selected section with lines3520-3522 is selected, the lines would be sorted with line 3520 first,line 3521 second, and line 3522 third. Various embodiments sort thelines by ascent, descent, baseline, or other measure of the verticalposition of a line.

The process then selects (at 3340) the top-most unevaluated line in thesection. Next, the process selects (at 3345) the first (reading from theleft for left-to-right languages) unevaluated character in the selectedline. The process determines (at 3350) whether the selected charactercan be merged into the next line. Some embodiments allow a character tobe merged into the next line when the selected character does nothorizontally overlap significantly with any character in the next line.Some embodiments allow some small amount of horizontal overlap betweencharacters. For left-to-right languages, some embodiments allow lessoverlap on the left of the character to be merged down than on the rightof the character to be merged down, in order to account for commonspacing adjustments for offset characters.

Furthermore, some embodiments allow any amount of overlap when theoriginal insertion order of the overlapping characters is adjacent. Theinsertion order, in some embodiments, is the order in which thecharacters are drawn on the page. Often (though not always), charactersare drawn in the order they are meant to be read, so when two verticallyand horizontally overlapping characters are adjacent in the insertionorder, it is likely they are intended to be read together.

When the process determines that the selected character can be mergedinto the next line, the process merges (at 3355) the selected characterin to the next line. The process then proceeds to 3365 which isdescribed below. Otherwise, when the selected character cannot bemerged, the process keeps (at 3360) the selected character in theselected line.

Next, the process determines (at 3365) whether the selected lineincludes more characters. When there are more characters in thecurrently selected line, the process proceeds to 3345 to select the nextunevaluated character in the line. Otherwise, when all characters in theline have been evaluated, the process determines (at 3370) whether thecurrent section includes more lines. When there are more lines in thecurrently selected section, the process proceeds to 3340 to select thenext unevaluated line.

Otherwise, when all lines in the section have been evaluated, theprocess determines (at 3375) whether the selected group includes moresections. When there are more sections in the currently selected group,the process proceeds to 3330 to select another section and merge linesin that section. Otherwise, when all the sections in the group have beenevaluated, the process determines (at 3380) whether there are any moregroups to evaluate in the document portion. When there are more groups,the process proceeds to 3320 to select another group. Otherwise, whenall groups have been evaluated, then line-merging is finished for thedocument portion and the process ends.

FIG. 35 illustrates the result of line merging for page 3500 in someembodiments. Line 3506 can merge down into line 3505, such that line3505 now includes the superscript “m” from line 3506, while line 3506 isempty and is therefore removed. Although there is no horizontal overlapbetween lines 3510 and 3511, the end of line 3510 is close enough to thestart of line 3511 that they are not partitioned, and all of line 3510can be merged down into 3511. Both characters in line 3516 are mergeddown into line 3515.

Lines 3520-3522 cannot be fully merged. The character “b” in line 3520is initially merged down into line 3521. Then, the character “A” in line3521 is merged down into line 3522 as it does not overlap with thecharacter “c”. However, character “b” is not merged down into line 3522because it completely overlaps with character “c”. Thus, line 3521 onlyincludes “b”, line 3522 includes “A” and “c”, and line 3520 is empty. Asdescribed above, some embodiments will merge “b” into line 3522 if “b”and “c” are adjacent in the insertion order.

Similarly, lines 3525 and 3526 are not merged. All of the characters inline 3526 significantly overlap one or more characters in line 3525, andtherefore are not merged down into line 3525. It is unlikely that the“T” in line 3526 would be between the “h” and “n” of line 3525 in theinsertion order for page 3500. Lastly, lines 3530 and 3531 are notmerged because there is no horizontal overlap between the lines and thusthey are partitioned at operation 3325.

After the lines are identified and merged, words are identified in someembodiments. Some embodiments use difference clustering, as described inU. S. Publication No. 2007/0250497 to identify words based on spacingbetween letters within a word and between words. In some embodiments,the difference clustering also provides information about segment gaps,column gaps, etc. Some embodiments use the memory and processingefficiency techniques described below in Section VI to performdifference clustering.

B. Identifying Words and Gaps Using Difference Clustering

FIG. 36 conceptually illustrates a process 3600 of some embodiments forperforming difference cluster analysis. Many forms of cluster analysisrequire foreknowledge of the number of groups/clusters since there mayexists multiple levels/hierarchies of clustering. For example, whenusing cluster analysis to group celestial objects, a specification ofthe number of clusters determines whether the cluster analysis willgroup objects on the level of stars, solar systems, galaxies, orsuperclusters. However when using cluster analysis to discover thestructural relationships between elements of content, e.g., the numberof groups are not known in many cases. For example, in the case of apage of text, it cannot be assumed the glyphs make up words, wordscombine to form lines, and groups of lines form paragraphs, because thedocument may have two or more columns of text such that a given initialline of text may include parts of two or more paragraphs.

In some embodiments, cluster analysis is a set of techniques that can beapplied to a collection of data points to group points into clustersthat are closer to each other than to the points of another cluster. Insome embodiments, cluster analysis is applied to data points thatrepresent the horizontal and vertical gaps between objects such asglyphs, words, and text lines. For example, some embodiments use k-meanscluster analysis, which will now be described. Starting with acollection of numbers (p₁, . . . , p_(N)) representing spatial gaps, anda known value for k (the number of clusters), the technique is used topartition the numbers into k clusters C₁, . . . , C_(k) defined byinequalities of the form C_(j)={p_(i)|a_(j)≦p_(i)<a_(j)+1} where a₁, . .. a_(k+1) is an increasing sequence. Before applying the k-meanstechnique, the differences p_(i+1)−p_(i) are sorted by size and the k−1largest differences are taken to be the partition points. For example,if p_(i+1)−p_(i) is one of the k−1 largest differences, then p_(i+1) isin a different cluster from p_(i), and p_(i+1) is one of the successivevalues a_(j). k-means cluster analysis is then applied to repeatedlyrefine the clusters. The k-means technique involves taking the mean ofthe numbers in each cluster, then re-distributing the p_(i) intoclusters by associating them with the closest calculated mean. This isperformed repeatedly until it causes no change in the clusters or theirmeans.

In some embodiments, a technique disclosed and referred to herein as“difference clustering” is used to determine the number of levels ofstructural relationships that exist between content elements comprisinga given source content and/or one or more hierarchical relationshipsbetween such levels, as well as one or more characteristics that can beused to determine whether a content element is related to anothercontent in each of the determined levels. In some embodiments,difference clustering utilizes the k-means technique together with othertechniques. In the example shown in FIG. 36, differences betweenpositions of content elements (spacing) are analyzed using differenceclustering analysis. In some embodiments, by analyzing the spacingbetween content elements, the content elements can be grouped at leastin part using the grouping data of the spacing. In some embodiments,each directional component of spacing is analyzed separately. Forinstance, difference clustering analysis on the horizontal component isused to distinguish between character spacing, word spacing, and columnspacing. Difference clustering analysis on the vertical component can beused to distinguish line spacing, paragraph spacing, and text boxspacing in some embodiments. Process 3600 conceptually illustratesdifference clustering analysis for a single directional component. Theprocess may be used again to analyze one or more additional directionalcomponents. In some embodiments, the results of performing differencecluster analysis along one or more dimensions are combined together todetermine the structural relationships between content elements at oneor more levels.

As shown in FIG. 36, process 3600 receives (at 3605) a portion of adocument. The process then identifies (at 3610) the locations ofelements in the document. In some embodiments, the elements includecharacters, glyphs, images, lines, drawings, boxes, cells, margins,and/or various other content elements. In some embodiments, locations ofthe elements include determining and/or assigning one or more locationcoordinate components to the elements. In some embodiments, thelocations of the elements are organized in an order. For example whenanalyzing the horizontal spacing of characters, the characters areorganized in increasing horizontal coordinate order for each line ofcharacters. In some embodiments, the location coordinate values of theelements are desired to be associated with the spacing between theelements, and the location values are compensated for the width/lengthof the element. For example, when determining a compensated horizontalcoordinate (x-coordinate) value for an element in the n-th position ofan organized order of elements, the following formula is used:

$X_{n}^{\prime} = {X_{n} - {\sum\limits_{i = 1}^{n - 1}W_{i}}}$

where X′_(n) is the compensated location coordinate value, X_(n) is theoriginal location coordinate value, and W_(i) is width of an element inthe i-th position. In some embodiments, the width of an element is basedon the character it represents, the font size, the styling of thecharacter, etc. Some embodiments determine a compensated locationcoordinate value by using known anchor coordinates for each character,and adjusting those coordinates for each particular character by thewidth of the particular character.

Next, the process determines (at 3615) the first-order differencesbetween locations of adjacent elements. In some embodiments, an elementis adjacent to another element when the two elements with at least onesame location coordinate component value are ordered next to each otherin at least one other location coordinate component value. For instance,two glyphs are adjacent to each other if both of the glyphs belong tothe same text line and no other glyph exists between them. In someembodiments, two elements have at least one same location coordinatecomponent when the difference between corresponding location coordinatecomponent values of the elements is below a limit value or within arange value. In various embodiments, an element is adjacent to anotherelement when the two elements are next to each other in an order and/ororganization associated with the identified locations of the elements.In some embodiments, the first order difference between the locations isthe difference between the width/length compensated location coordinatevalues. For instance, when determining the difference betweencompensated horizontal coordinate (x-coordinate) values for the adjacentelements in the nth and n+1 position of an organized order ofcompensated horizontal coordinates, in some embodiments the followingformula is used.

ΔX _(n) =X′ _(n+1) −X′ _(n)

In some embodiments, the first order difference is associated with thegap spacing between glyphs in the content.

Next, process 3600 sorts (at 3620) the first order differences. In someembodiments, organizing the first order difference includes ordering thefirst order differences in an increasing order. In some embodiments,organizing the first order differences includes assigning a weight valueto one or more of the first order differences and organizing the firstorder differences at least in part by using the weight value(s). Forinstance, in some embodiments, actual glyph spacing is divided byexpected glyph spacing for each specific pair of glyphs given the fontthat is used and its font metrics including size, default letterspacing, and a table of kerning values stored with the font file. Thisratio of actual to expected spacing is ordered by increasing value, andthe values of this ratio are used in place of the first orderdifferences throughout the remainder of the difference clusteringmethod.

The process then determines (at 3625) second order differences betweenthe sorted first order differences. For instance, when determining thesecond order difference between first order differences in an i-th andi+1 position of an organized order of first order differences, thefollowing formula is used:

Δ² X _(i) =ΔX _((i+1)) −ΔX _((i))

where Δ²X_(i) is the i-th second order difference, ΔX_((i)) is the firstorder difference in the i-th position of the sorted first orderdifferences, and ΔX_((i+1)) is the first order difference in the i+1position of the same sorted first order differences. In someembodiments, the second order differences are associated withdifferences between the spacing of glyphs.

Next, process 3600 determines (at 3630) the number of cluster levels byanalyzing the second order differences. In some embodiments, analyzingthe second order differences includes organizing the determined secondorder differences. In some embodiments, organizing the second orderdifference includes ordering the second order differences in anincreasing order and/or plotting the second order differences in anorder of increasing second order difference values. In some embodiments,organizing the second order difference includes assigning a weight valueto one or more of the second order difference. In some embodiments,organizing the second order difference includes grouping the secondorder differences into one or more groups. In some embodiments, thesecond order differences are each categorized as either an inter-groupdifference or an intra-group difference.

Intra-group differences are associated with relatively smaller secondorder difference values and can represent second order differences offirst order differences within the same clustering group. An example ofan intra-group difference is the relatively small variation one wouldexpect to find in the character-width compensated spacing betweenletters in the same word. Inter-group differences are associated withrelatively larger difference values and can represent second orderdifferences of first order differences between different clusteringgroups. An example of an inter-group difference is the relatively largedifference between the space between two words, on the one hand, and thespace between two letters in the same word, on the other.

In some embodiments, the categorization of second-order differences intointra-group and inter-group values is achieved by applying 2-meanscluster analysis to the ordered second-order difference values;specifically, taking (p₁, . . . , p_(N)) to be {Δ²X₁, . . . , Δ²X_(N)}in increasing order. Similarly, any other technique of cluster analysisthat is sufficient to distinguish two clusters of data values can beapplied to the ordered second-order difference values. The intra-groupdifferences are then in the first cluster C₁={p_(i)|a₁≦p_(i)<a₂}, andthe inter-group differences are in the second clusterC₂={p_(i)|a₂≦p_(i)<a₃}, where a₁<a₂<a₃. In some embodiments, the numberof levels into which content elements are determined to be organized,based on their spatial relationships analyzed as described above, is onemore than the number of inter-group differences found through differencecluster analysis. For instance, when two inter-group differences exist,the number of structural levels is three. Taking a simple example,consider characters that form words comprising a single line of text.The first order differences in the spacing between characters in thex−x-direction would yield a second order difference between characterspacing and word spacing (one inter-group difference), indicating twolevels of structure (words and lines). When the text had been in twocolumns, a further second order difference (between word spacing andcolumn spacing) would have been detected, for a total of two inter-groupdifferences, indicating three structural levels in the x-direction(words, lines, and columns). Repeating the analysis in the y-directionand combining results would, when applicable to the particular content,identify in some embodiments any further structural levels (e.g.,paragraphs, etc.) that are manifested in the spacing between charactersand groups of characters.

The process then determines (at 3635) characteristics of each clusterlevel. The process then ends. In some embodiments, determining thecharacteristics includes determining which first order difference(and/or what range of first order differences) is associated with whichcluster level. In some embodiments, determining the characteristicincludes computing a statistical value associated with the first orderdifferences associated with a cluster level. For example, by determiningthe average, minimum, maximum of the portion of first order differencesassociated with a cluster level, the average, minimum, and maximumspacing between glyphs in the content can be determined.

Let L be the number of levels of clustering. In some embodiments, L iscomputed by counting the number of points in the second cluster ofsecond-order differences and adding 1. Next, the groups of first-orderdifferences corresponding to each level can be identified, and theclusters of compensated X_(n)′ values can be identified at each level,for example, in one of the following two ways.

One possibility is to perform L-means cluster analysis on thefirst-order differences. The resulting L clusters are the groups offirst-order differences corresponding to each level. Next the numberK_(m) of clusters of X_(n)′ at level m are computed by adding the numberof points in the (m+1)th, (m+2)th, . . . , and Lth clusters offirst-order differences plus 1. Finally, perform K_(m)-means analysis onthe compensated X_(n)′ values to produce the K_(m) clusters at level m.

A second possibility is, when originally computing each first-orderdifference ΔX_(n)=X_(n+1)′−X_(n)′, to store its value together with theindex n that can be used to identify either one of the pair ofsuccessive X values that were subtracted to produce that difference.Store the value and the index reference in a single “first-orderdifference” data structure. Similarly, when originally computing eachsecond-order difference, store its value together with an indexreference that can be used to identify either one of the pair ofsuccessive “first-order difference” data whose values were subtracted toproduce that difference. Now, for each second-order difference that isin the second cluster (i.e. for each inter-group difference), use itsindex reference to identify a partition point in the first-orderdifferences. This means that the index identifies a pair of first-orderdifference values that are partitioned to be in separate clusters.Partitioning in this way produces L clusters of first-order differencescorresponding to the L levels of clustering in the original data. Now,the clusters of X_(n′) values at level n are identified as follows: foreach first-order difference data in the (m+1)th, (m+2)th, . . . , andLth cluster of first-order differences, use its index reference as apartition point in the X_(n)′ values.

FIG. 37 illustrates an example of difference clustering. In someembodiments, the example of FIG. 37 is associated with process 3600 ofFIG. 36. Groups of first order difference values 3705, 3710, and 3715are plotted in order from lowest value to highest value on a lineassociated with first order difference values. Each point is associatedwith a difference value, e.g., the distance from a text character orother glyph to an adjacent one, and in FIG. 37 the points are notsuper-imposed on top of each other to illustrate the example clearly.

In the example shown, the data are associated with horizontal spacingbetween glyphs. By ordering the first order difference values, theexample illustrates three groups of first order difference values 3705,3710, and 3715. First order difference value group 3705 is associatedwith spacing between glyphs that compose words. First order differencevalue group 3710 is associated with spacing between words. First orderdifference value group 3715 is associated with spacing between columns.For each pair of adjacent first order difference values, a second orderdifference value (i.e., the difference between one first orderdifference and an adjacent first order difference) is determined andplotted in an increasing order on a line associated with second orderdifference values. Second order difference value group 3720, 3725, and3730 each include one or more points associated with the second orderdifference values. In some embodiments, point 3725 is a member of agroup of associated second order difference points comprising a secondorder difference value group.

In some embodiments, point 3730 is a member of a group of associatedsecond order difference points comprising a second order differencevalue group. In some embodiments, 3720 is identified as one cluster and3725 together with 3730 is identified as a second cluster. Second orderdifference values between the first order difference values within thesame single first order difference value group (intra-group differences)are included in second order difference value group 3720. In a textdocument, for example, typically the character-width compensated spacingbetween characters within a word, or in the spacing between differentpairs of words, varies only slightly. The second order differencebetween inter-group adjacent points in group 3705 and 3710 is includedin point 3725. The second order difference between inter-group adjacentpoints in group 3710 and 3715 is included in point 3730. Since thereexists two inter-group second order difference values in the example,there are two plus one (three) grouping levels (in this example, words,sentences or parts thereof on a line of text within a column, andcolumns). By determining the minimum and maximum of the first orderdifference values in group 3705, minimum and maximum spacing betweenglyphs that compose words can be determined, and similarly group 3710and 3715 can be used to determine word spacing and column spacingrespectively.

In some embodiments, the minimum and maximum spacing associated witheach grouping level is used to group content elements (e.g., glyphs)accordingly, such as by identifying groups of characters that comprisewords, group words into lines of text within a column, etc. By usingdata determined from cluster analysis, the glyphs are grouped into thedetermined levels of groupings. It is possible to perform the analysisquickly and automatically with respect to any arbitrary content, in partbecause it is not necessary to know in advance how many grouping levelsthere are in the structure of the content or other collection ofelements being analyzed. Regardless of the number of grouping levels,the number of levels is determined in just two processing steps. Bydetermining the average of the first order difference values in group3705, the average spacing between glyphs that compose words can bedetermined. Similarly, other statistical quantities can be determinedfor the glyphs that compose words. Similarly, an analysis of the firstorder difference values in group 3710 and 3715 can be used to determinestatistical quantities relevant to word spacing and column spacing.

C. Splitting Lines

Some embodiments split text lines after word and segment breakinformation is generated. Text lines are split, for example, where thetext line spans more than one column, as the text in the two (or more)sections is probably not meant to be read together. Some embodiments useguide and gutter information derived from processes described above inSection III along with information from difference clustering (e.g.,segment gaps, etc.) in order to split the text lines.

FIG. 38 conceptually illustrates a process 3800 of some embodiments forsplitting lines of text. Portions of process 3800 will be described inconjunction with FIG. 39. FIG. 39 illustrates a sequence that shows theidentification of where lines on a page 3900 should be split. As shownin FIG. 38, process 3800 receives (at 3805) text lines, guide and gutterinformation, and segment break information for a portion of a document.Text line information is the output of process 3300 in some embodiments,and guide and gutter information is the output of processes 2100, 2500,and 2600 in some embodiments. The segment break (or segment gap)information is one of the outputs of difference clustering as describedin the above mentioned U. S. Publication No. 2007/0250497, as well asabove, in some embodiments. In some embodiments, the document portion isthe entire document, a section, a page, or a zone of a page.

Next, the process sorts (at 3810) the received text lines based on they-coordinate of their baselines. Starting at the bottom of the page, theprocess selects (at 3815) the bottom-most unevaluated text line andidentifies (at 3820) potential splits in the selected line. Someembodiments define a potential split as any gap between two words in aline either (1) is a segment gap, as defined by difference clustering,or (2) has a guide or gutter passing through it. Other embodiments onlyuse one or the other, or different definitions, for potential splits.

The process then determines (at 3822) whether any potential splits wereidentified. When none were identified, the process proceeds to 3845,described below. Otherwise, the process selects (at 3825) a potentialsplit from the currently selected text line. The process then determines(at 3830) whether the x-interval of the potential split overlaps withthe x-interval of any potential split from the previous text line. Thefirst text line evaluated will not have a previous text line, andtherefore there will be no overlapping potential splits. When thex-interval of the currently selected potential split does not overlapwith the x-interval of a potential split from the previous line, theprocess proceeds to 3822 which was described above. Otherwise, theprocess associates (at 3835) the overlapping potential splits. Theprocess then proceeds to 3822 which was described above.

When there are no more unevaluated potential splits, the processdetermines (at 3845) whether there are more lines to evaluate. When morelines remain, the process proceeds to 3815 to identify potential splitsin the next line and test them for overlap.

When all lines have been evaluated, then all the potential splits in thedocument portion have been identified and associated. The process thenperforms several operations to eliminate false positives (i.e.,potential splits that should not actually split a line of text). Theprocess determines (at 3847) whether any groups of potential splits wereidentified. When none were identified, the process ends. Otherwise, theprocess selects (at 3850) a group of associated potential splits anddefines (at 3855) a rectangular strip passing completely through thepotential splits of the selected group. The strip, in some embodiments,has an x-interval that is the intersection of the x-intervals of all thepotential splits in the selected group (i.e., the x-interval for a striptwo of whose potential splits barely overlap will be very thin).

FIG. 39 illustrates a page 3900 with several lines of text. Most of thelines of text are split between two columns. However, the baselines arethe same in either column. Therefore, each line from the first columnwould be in the same line as a line from the second column prior to theline-splitting process. FIG. 39 also illustrates four rectangular strips3905, 3910, 3915, and 3920 of associated potential splits.

After defining the rectangular strip for the selected group, the processdetermines (at 3860) whether the strip spans fewer than a thresholdnumber of text lines. Strips that span one or only a few text lines arenot likely to represent an actual split in reading, but rather may betabs within a line or other non-breaking gaps. Sometimes segment gapsare found by difference clustering where a gap between words is verylarge due to justified text. When the strip spans fewer than thethreshold number of lines, the process removes (at 3865) the group fromthe list of potential splits and will not split the text lines at thoselocations. The process then proceeds to 3890 which is described below.On page 3900, the potential splits making up strips 3910 and 3920 areremoved because they do not have enough splits to be a likely columnbreak. More likely, the potential splits are tabs or large word gaps.

When the strip spans at least the threshold number of lines, the processdetermines (at 3870) whether the current strip is within a thresholddistance of another strip. Some embodiments only look to prior stripsthat have been tested and not yet removed when determining whetheranother strip is within a threshold of the current strip. When thecurrent strip is within the threshold distance of another strip, theprocess removes (at 3875) the group with a vertically shorter strip (insome cases, where the lines are all the same size, this is the stripthat spans fewer text lines). The process then proceeds to 3890 which isdescribed below.

Strips 3905 and 3915 of page 3900 both qualify as spanning enough textlines to pass operation 3860. However, in some embodiments the stripsare too close to each other to both be kept. Accordingly, the group ofpotential splits making up strip 3905 is removed because 3915 is thelonger of the two strips. This process prevents list bullets or numberfrom being split from the items they reference, in some embodiments, aswell as other potentially problematic splits.

When the current strip is not too close to another strip, the processdetermines (at 3880) whether the strip includes a threshold number ofsubsequent potential splits in a row that are not segment gaps. In someembodiments, it is possible to identify a guide and/or gutter where wordedges accidentally align. This is especially likely if the text isdisplayed in a monospace font (e.g., Courier). When the strip includesat least this threshold number of subsequent non-segment gap potentialsplits, the process removes (at 3885) the group from the list ofpotential splits and will not split the text lines at those locations.

Next, the process determines (at 3890) whether there are more groups ofpotential splits that have not been tested against the various thresholdrequirements. When more groups remain, the process proceeds to 3850 toselect and evaluate the next group of potential splits. Otherwise, whenall groups have been evaluated, the process splits (at 3895) the textlines using any of the splits that have not been removed. The processthen ends. In the case illustrated for page 3900, the only splits thatwould be used are those in the center separating the two columns oftext.

While process 3800 is illustrated using three specific tests (operations3860, 3870, and 3880) to remove groups of potential splits, someembodiments employ only a subset of these, while other embodiments useother tests that are not shown in order to eliminate potential splitsfrom consideration.

D. Paragraph Identification

In some embodiments, once lines of text have been merged and split, thelines are grouped into paragraphs. FIG. 40 conceptually illustrates aprocess 4000 of some embodiments for grouping text lines intoparagraphs. Portions of process 4000 will be described in conjunctionwith FIG. 41. FIG. 41 illustrates the identification of paragraphs on apage 4100 of a document. As shown in FIG. 40, process 4000 receives (at4005) text lines for a portion of a document. The text lines havealready been merged (e.g., by process 3300) and split (e.g., by process3800) in some embodiments before process 4000 is performed. In someembodiments, the document portion is an entire document, a section of adocument, a page, a zone, etc.

The process determines (at 4007) whether there are any lines in thedocument portion. When there are none, the process ends. Otherwise,beginning at the top of the received document portion, the processselects (at 4010) the first unevaluated text line in the documentportion. The process then determines (at 4015) whether there is morethan one text line below the selected line. In some embodiments, thelines must be within a particular vertical distance of each other forthe lower line to be considered below the selected line for the purposesof operation 4015. Some embodiments require at least three text lines tomake judgments about whether the text lines belong to the sameparagraph. In some embodiments, this requirement is imposed because twospacings (i.e., the spacing between the first and second text lines andbetween the second and third text lines) are necessary in order to makea comparison.

When there are two or more lines below the selected text line, theprocess proceeds to 4030 which is described below. Otherwise, when fewerthan two lines are below the selected text line, the process places (at4020) the selected line in a paragraph by itself. The process thendetermines (at 4025) whether there are more lines in the documentportion. When there are no more lines (e.g., when there is only one lineof text in the document portion), the process ends. Otherwise, whenthere are more lines, the process proceeds to 4010 and selects the nextline of text.

When, at 4015, there are two or more lines of text below the lineselected at 4010 (i.e., the first line in the current paragraph), theprocess identifies (at 4030) the next two lines below the selected textline. The process then determines (at 4035) whether the spacing andalignment is consistent between the three lines. In some embodiments,this determination involves examining whether the vertical distance fromthe first to second line is the same as the vertical distance from thesecond to third line. Some embodiments use the baselines of the textlines to determine the vertical spacing. Alignment differences, in someembodiments, are identified if one of the lines begins indented, or endsleft of the other lines, thus signaling a likely beginning or end of aparagraph.

When the spacing and alignment is not consistent, the process applies(at 4040) heuristic rules to determine whether to add either of theidentified lines to the paragraph with the selected first line. Forinstance, in some embodiments, when the first two lines are closetogether and the third line is further down, the first two lines areplaced in one paragraph and the third line is the start of the nextparagraph. Similarly, in some embodiments, when the first line isfurther from the second and third, the first paragraph is a one-lineparagraph and the next paragraph starts at the second line. Similarrules are used in some embodiments for alignment differences between thelines. After applying the heuristic rules, the process proceeds to 4010to select the next unevaluated text line (i.e., the next line that isnot yet assigned to a paragraph) and start a new paragraph.

When the spacing and alignment is consistent between the three lines,the process places (at 4045) all three lines in the same paragraph. Someembodiments identify spacing and alignment properties of the paragraphas well. For instance, some embodiments identify paragraphs asleft-aligned, right-aligned, justified, centered, etc. Some embodimentsleave open multiple possibilities (e.g., a paragraph with an indentedfirst line, all three lines right-aligned or very close, and the lowertwo lines left-aligned could possibly be any of the three ofleft-aligned, right-aligned, or justified).

After the initial phase of identifying the start of a new paragraph,process 4000 attempts to add lines to the paragraph. In someembodiments, the line addition is based on the spacing and alignmentproperties determined from the three lines making up the start of theparagraph. In other embodiments, as lines are added that do not conflictwith the spacing and alignment properties for the paragraph, the spacingand alignment properties are refined based on any further evidence.

Next, the process determines (at 4047) whether there are any more linesin the document portion. When there are no more lines (i.e., thedocument portion has exactly three lines), the process ends. Otherwise,the process identifies (at 4050) the next text line in the documentportion. The process then determines (at 4055) whether there is aspacing or alignment mismatch between the current paragraph and theidentified next line. When there is a mismatch, the process ends theparagraph and proceeds to 4010, which was described above. In such acase, the recently mismatched line will be the line selected at 4010.

Otherwise, when the spacing and alignment line up, the process adds (at4060) the line to the current paragraph. The process then proceeds to4047, which was described above. In some embodiments, an alignmentmismatch is found when the identified next text line does not fit one ofthe properties (e.g., justified) of the paragraph. Similarly, if thespacing between the last line in the paragraph and the next line isincreased as compared to that of the paragraph, then a spacing mismatchis found in some embodiments.

Some embodiments employ other stopping conditions (e.g., conditionsresulting in the identified line not being added to the paragraph). Forinstance, some embodiments recognize if the first word on the identifiedline would fit into the white space at the end of the last line of aleft-aligned paragraph. When this is the case, the new line is assumedto be part of the next paragraph because if it were part of the currentparagraph, then the word would be in the white space at the end of thelast line rather than starting a new line. Similarly, some embodimentsrecognize an indent as indicating a new paragraph. A third condition ofsome embodiments is if the identified line is uniformly styled (e.g.,all bold, or of a larger font size) and different from the styling ofany character on the previous line.

Once process 4000 has completed, all of the paragraphs in the documentportion are identified, and all lines of text are assigned to aparagraph. Some embodiments then use the paragraphs to identify columnsand layouts.

FIG. 41 illustrates a page 4100 with four paragraphs. Applying process4000 to this page (where the page is the document portion) results inthe identification of paragraphs 4105, 4110, 4115, and 4120. The processof some embodiments would start by grouping the first three linestogether, then adding the fourth and fifth lines, until the sixth line4125 had a spacing and alignment mismatch, leaving paragraph 4105 atfive lines. The process would then start with the sixth line, and noticethe spacing and alignment mismatch between the two lines below. As linesix is further from lines seven and eight than they are from each other,line six is the entirety of paragraph 4110 and the next paragraph 4115starts with line seven. Paragraphs 4115 and 4120 are identifiedsimilarly.

E. Column and Layout Identification

Some embodiments place paragraphs into columns and layouts afteridentifying the paragraphs. In some embodiments, a column is avertically ordered group of paragraphs in which the text readscoherently from the top to the bottom. A layout in some embodiments is acollection of non-overlapping columns and a linear layout in someembodiments is a horizontally ordered group of columns in which the textreads coherently from the top of the left-most column to the bottom ofthe right-most column. For example, some embodiments classify a simplepage with unsegmented text lines and no headers or footers as a singlelinear layout with one column.

FIG. 42 conceptually illustrates a process 4200 for identifying columnsand layouts in a portion of a document in some embodiments. Process 4200will be described in conjunction with FIGS. 43-46. FIGS. 43 and 44illustrate paragraphs on two different pages 4300 and 4400, and FIGS. 45and 46 illustrate the generation of flow graphs for the two pages 4300and 4400 respectively.

As shown in FIG. 42, process 4200 receives (at 4205) information forparagraphs for the portion of the document. The document portion in someembodiments is an entire document, a section of a document, a page, azone, etc. In some embodiments the paragraph information is determinedusing process 4000 described above. The process then determines whetherthere are any paragraphs to select. When there are none, the processexits.

Otherwise, the process selects (at 4210) a paragraph. In someembodiments, the paragraphs in the document portion are selected inorder, starting at the top-left, whereas in other embodiments theparagraphs are selected in a random order.

Next, the process calculates (at 4215) the in-order, out-order,left-order, and right-order, as well as sets of paragraphs thataccompany each of these values. The out-order of a paragraph p iscalculated in some embodiments by using a set B(p). The set B(p) isinitially all paragraphs below paragraph p in the document portion thatoverlap p horizontally (i.e., that overlap x-coordinates). For instance,FIG. 43 illustrates a page 4300 with eleven paragraphs includingparagraph P 4305. The set B(P) is initially {Q, R, S, T, U}. Next, theparagraph closest to p is identified as q, and all paragraphs thatoverlap paragraph q horizontally are removed from the set B(P). In thecase of paragraph P 4305, paragraph Q 4310 is the closest to paragraphP, and paragraphs R 4315, S 4320, T 4325, and U 4330 are removed fromthe set B(P). At this point, the set B(P) is {Q}.

Some embodiments then continue onto the next closest paragraph to p thatwas initially in the set B(p), and remove any paragraphs from B(p) thatare below and horizontally overlap this next closest paragraph. Otherembodiments continue to the next closest paragraph to p that remains inthe set B(p), and remove any paragraphs from B(p) that horizontallyoverlap this paragraph. Either way, in the example of FIG. 43, the setB(P) for paragraph P 4305 is {Q}. The out-order of p is then thecardinality (i.e., number of elements) of the set B(p). This is repeatedfor each paragraph in B(p). Thus, in this case the out-order ofparagraph P 4305 is 1. As an example of a paragraph with an out-ordergreater than 1, for paragraph R 4315, the set B(R) is {S, X}, so thatthe out-order of paragraph R 4315 is 2.

The in-order of a paragraph p is calculated similarly to the out-orderin some embodiments by using a set A(p). The set A(p) is initially allof the paragraphs in the document portion above p that overlap phorizontally. The closest paragraph top is selected as paragraph q, andthe paragraphs that overlap paragraph q horizontally are removed fromA(p). This is then repeated for each of the paragraphs in A(p). In theexample page 4300, the set A(P) for paragraph P 4305 is the empty set,while the set A(R) for paragraph R 4315 is {Q, W}. The in-order of aparagraph p is the cardinality (i.e., number of elements) of the setA(p).

The left-order and right-order of a paragraph p are also calculatedsimilarly in some embodiments, using a set L(p) (paragraphs left of pand vertically overlapping p, using the same removal rules) and a setR(p) (paragraphs right of p and vertically overlapping p, using the sameremoval rules). Some embodiments use L(p) and R(p) for flow graphs (seebelow) when it has been determined (e.g., by an external means) that thelanguage direction is top-down. For page 4300, the set R(P) forparagraph P 4305 is {V}, while the set L(V) for paragraph V 4335 is {P}.The sets L(R) and R(R) for paragraph R 4315 are both empty.

Once the in-order, out-order, left-order, and right-order are calculatedfor the selected paragraph, the process 4200 determines (at 4220)whether more paragraphs remain for which the various values must becalculated. If more paragraphs remain, the process proceeds to 4210 toselect another paragraph.

Otherwise, once the values are calculated for all paragraphs, theprocess generates (at 4225) a flow graph for the paragraphs. The flowgraph of some embodiments is generated such that each paragraph in thedocument portion being evaluated is a node. A directed edge is drawnfrom the node for a paragraph p to each node for the paragraphs in theset A(p). This is the same, in some embodiments, as drawing a directededge from each node for the paragraphs in the set B(p) to the node forthe paragraph p. FIG. 45 illustrates an initial flow graph 4501 for thepage 4300.

Next, process 4200 identifies (at 4230) call-outs. In some embodiments,identified call-outs are removed from the flow graph. A call-out, insome embodiments, is a text element on a page that is meant to be readin an order independent from the rest of the text on the page. Someexamples of call-outs include headers and footers, footnotes, marginnotes, side-bars, and other blocks of text placed amongst other elementssuch as large-font quotes in a magazine article.

Some embodiments identify call-outs based on a combination of thegeometry of the text element, its position on the page, its flowproperties (in-order, out-order, left-order, and right-order), and thestyle properties of its elements. For instance, when a vertex v includesa one-line paragraph that is close to the top of a page, the distancefrom the one-line paragraph to any element in A(v) is more than one lineheight, L(v)≦1, R(v)≦1, and any vertices in L(v) and R(v) share theseconditions, then some embodiments classify the paragraph as a headercall-out. Requirements for a footer call-out are similar in someembodiments, except looking for the distance to the bottom of the pageand to elements in B(v).

Some embodiments also identify sidebars that jut into columns (and arenot in their own zone), randomly located text boxes, small bits of textwith no obvious relationship to other text (e.g., figure captions), etc.as call-outs. Some embodiments make these determinations (as well asother determinations of flow properties) based on a purely textualanalysis, whereas other embodiments incorporate images into the analysis(e.g., as further evidence for a figure caption). For example, in someembodiments, some embodiments identify single-line paragraphs distantfrom all elements in A(p) and B(p) as isolated small paragraphs.Captions are identified in some embodiments when a paragraph with asingle text line is enclosed by the bounds of an image and is aligned inparticular ways with the image bounds (e.g., centered near the bottom,centered near the top, etc.).

When the rectangular bounding boxes of two or more paragraphs intersect,some embodiments identify all but one of the paragraphs as intersectioncall-outs. For instance, suppose that two paragraphs p and q overlap andB(p)={q, r}. When r has an in-order of 1 or when q is in A(r), then q isan intersection call-out in some embodiments. Some embodiments classifyas an intersection call-out any paragraph p whose style and/or alignmentproperties are not consistent with the paragraphs in A(p) or B(p). Whentwo paragraphs intersect, and none of the above rules applies, someembodiments classify the paragraph with smaller area as a call-out.

After generating the flow graph for the paragraphs in the documentportion, the process 4200 merges (at 4235) nodes of the flow graph intocolumns. Some embodiments merge nodes for paragraphs p and q if A(p)={q}and B(q)={p}. This indicates that paragraphs p and q are in the samecolumn in some embodiments. In some embodiments, the new node pq willhave A(pq)=A(q), B(pq)=B(p), L(pq)=L(p)+L(q), and R(pq)=R(p)+R(q). Forexample, in FIG. 45, the flow graph 4501 is modified such that nodes S4520, T 4525, and U 4530 are merged into node STU 4575 in modified flowgraph 4502. The other nodes are modified similarly.

FIG. 46 illustrates a flow graph 4601 for the page 4400 of FIG. 44 afterthe nodes have been merged initially into columns. Some embodimentsidentify paragraph R 4420 as a call-out because it straddles two columnsand has paragraphs to both the left and right. Accordingly, someembodiments remove the node R 4620 from the flow graph 4601. Thisenables further merger of the nodes into columns.

Once call-outs have been identified (and, in some embodiments, removedfrom the flow graph), process 4200 partitions (at 4240) the flow graphinto layouts. Some embodiments define labels for expansion and reductionedges as part of the partitioning process. In some embodiments, if theout-order of a paragraph p is greater than 1, and the in-order of eachparagraph q in the set B(p) is 1, then the edge from p to each q in B(p)is an expansion edge. Similarly, in some embodiments, if the in-order ofa paragraph p is greater than 1, and the out-order of each paragraph qin the set A(p) is 1, then the edges from each q in A(p) to p is areduction edge. FIG. 45 illustrates that the edges leading into node R4515 are both reduction edges, and the edges leading out of node R 4515are both expansion edges.

The partitioning of some embodiments examines each vertex v the edges ofwhich are all labeled. When the in-order of v is greater than 1, someembodiments define a partition the elements of which are B(v) so long asA(p)={v} for each p in B(v). Similarly, when the out-order of v isgreater than 1, some embodiments define a partition the elements ofwhich are A(v) so long as B(p)={v} for each p in A(v). When both ofthese partitions are possible, the vertex v is defined as a partition byitself. Based on these rules, the flow graph 4502 is partitioned intothree partitions 4511, 4512, and 4513.

Some embodiments place any remaining nodes into one or more partitionssuch that the smallest number of partitions is defined without anygeometric overlap between the partitions. Due to complex page structure,some embodiments use more relaxed partitioning rules than thosedescribed above. For instance, when a partition could be created from anode v, except that the out-order of v is greater than 1, then elementsof A(v) that are far from v and narrow relative to v are eliminated insome embodiments. When only one element remains in A(v), the edges fromv to the removed vertices are removed, and partitioning is continued.Once partitioning is complete, the process 4200 ends.

In some embodiments, each partition corresponds to a linear layout, andeach of the final (merged) nodes corresponds to a column. Oncepartitions are defined, some embodiments calculate properties of thedocument portion such as gutter width, margins, in-line or floatingimages, etc.

Furthermore, layout and flow information (including word, line,paragraph, and column data) is used prominently in the display of thedocument and enabling more robust user interaction with the document, asdescribed in further detail in the concurrently filed United StatesPatent Application with Attorney Docket No. APLE.P0143, entitled“Identification, Selection, and Display of a Region of Interest in aDocument”, which is incorporated herein by reference. For instance, insome embodiments, a user might wish to view a complex document thatincludes several columns of text, images, call-outs, captions, etc., andbe able to copy and paste the entire text of the document into a texteditor. In order for this to be accomplished, a reading order isassigned to each of the elements in the document that attempts toidentify the order in which a human would read through the elements ofthe document.

For instance, some embodiments assign reading orders to columns, suchthat the reading order follows the expected order in which a human wouldread the columns from the start to end of the document or page. Otherembodiments assign reading orders to other structural elements (e.g.,paragraphs, words, etc.). In some embodiments, when the user copies andpastes the entire text of such a document into another application, thetext appears in the application in the order that a human would read it.This is in contrast to copying and pasting from a standard PDF file thatorders all text in a strict top-down configuration.

Some embodiments also insert images and shapes into the reading order.For instance, some embodiments will identify a particular image asassociated with a particular column of text and insert the image eitherbefore or after (depending on the evidence in the document) the columnof text. As an example, some embodiments identify that an image isassociated with the caption for the image and insert the image into thereading order immediately prior to its caption.

Some embodiments also define links between structural elements. Forinstance, some embodiments use the reading order to define links betweena paragraph at the end of a column and a paragraph at the beginning ofthe next column that are actually one paragraph. In some embodiments, tomaintain the hierarchy that has each paragraph assigned to oneparticular column, a separate paragraph bridging the columns is notdefined. Instead, a link between the two paragraphs is definedindicating that they are, in fact, one paragraph. Some embodiments usetests similar to those for adding lines to a paragraph in order todetermine whether the top paragraph from a second column is actually acontinuation of the paragraph at the end of a first column (i.e.,examining spacing, alignment, font stylings, etc.). The link can then beused, e.g., if a user performs a selection operation (e.g., atriple-click) intended to select a paragraph within either of thedefined paragraphs, the entire actual paragraph will be selected basedon the link.

Some embodiments also define links between layouts (e.g., linking acrosspages) or zones. For instance, some embodiments can recognizecontinuation text (e.g., text in a newspaper indicating that a storycontinues on a different page) and can link the text in the layout withthe continuation text to the layout where the text continues. Someembodiments only attempt such linking when a profile has been matchedindicating that linking should be performed. For instance, if a documenthas been identified as a newspaper, then some embodiments will searchfor continuation text.

E. Software Architecture

In some embodiments, the layout and flow analysis processes describedabove are implemented as software running on a particular machine, suchas a computer, a media player, a cell phone (e.g., an iPhone®), or otherhandheld or resource-limited devices (or stored in a computer readablemedium). FIG. 47 conceptually illustrates the software architecture of alayout and flow analysis application 4700 of some embodiments foridentifying layout and flow characteristics of a document. In someembodiments, the application is a stand-alone application or isintegrated into another application (e.g., a document reconstructionapplication), while in other embodiments the application might beimplemented within an operating system.

Layout and flow analysis application 4700 includes a line identificationmodule 4705, a line-merging module 4710, a word identification module4715, a difference clustering module 4720, a line splitting module 4725,a paragraph identification module 4730, a column and layoutidentification module 4735, and an order calculator 4740.

FIG. 47 also illustrates document content 4745. Line identificationmodule 4705 receives information from the document content 4730. In someembodiments, this information is information about the position ofcharacters in the document. Line identification module 4705 identifiescharacters with a common baseline on a page and assigns them to a line.The line identification module passes information to, and receivesinformation from, line merging module 4710. The line merging moduleidentifies groups of lines that overlap vertically and determineswhether the lines should be merged. In some embodiments, line mergingmodule 4710 performs some or all of process 3300 described above. Theline merging module 4710 passes this information back to lineidentification module 4705, which identifies the final text lines. Lineidentification module 4705 passes the line information back to thedocument content 4745, as well as to line splitting module 4725.

Word identification module 4715 also receives information from thedocument content 4745. In some embodiments, this information isinformation about the position of characters in the document. The wordidentification module 4715 identifies characters that should be groupedtogether as words. Word identification module 4715 passes informationto, and receives information from, the difference clustering module4720. Difference clustering module 4720 performs difference clusteringon the document characters to return different levels of gaps betweencharacters (e.g., word gaps, segment gaps, etc.). The wordidentification module 4715 uses the difference clustering results toidentify the words. Word identification module 4715 passes its results(as well as other difference clustering results such as segment gaps) tothe document content 4745, as well as to line splitting module 4725.

Line splitting module 4725 receives line information from the lineidentification module and gap information from the word identificationmodule, as well as other information (e.g., gutter information) from thedocument content 4745. Line splitting module 4725 identifies where linesshould be split and outputs new line information based on the splits.The new line information is passed to document content 4745 as well asparagraph identification module 4745. In some embodiments, linesplitting module 4725 performs some or all of process 3800.

Paragraph identification module 4730 receives line information from linesplitting module 4725 as well as other information (e.g., alignmentinformation) from document content 4745. Paragraph identification module4730 identifies which lines should be grouped into paragraphs andoutputs the result information. The paragraph information is passed todocument content 4745 as well as to the column and layout identificationmodule 4735. In some embodiments, paragraph identification module 4730performs some or all of process 4000.

Column and layout identification module 4735 receives paragraphinformation from paragraph identification module 4730, as well as otherinformation (e.g., zone information) from document content 4745. Columnand layout identification module 4735 groups paragraphs into columns andgroups columns into layouts. Column and layout information module 4735passes information to, and receives information from, order calculator4740. The order calculator 4740 receives paragraph information from themodule 4735, and calculates the in-order, out-order, left-order, andright-order (as well as the corresponding sets A, B, L, and R) for theparagraphs. This information is then returned to the module 4735 for usein generating a flow graph. The results from column and layoutidentification module 4735 are passed to the document content 4745. Insome embodiments, column and layout identification module 4745 performssome or all of process 4200 described above.

In some embodiments, the results of the processes performed by theabove-described modules or other modules are stored in an electronicstorage (e.g., as part of a document object model). The document objectmodel can then be used for displaying the document on an electronicdisplay device (e.g., a handheld device, computer screen, etc.) suchthat a user can review and/or interact with the document (e.g., viatouchscreen, cursor control device, etc.).

V. Joining Graphs

In some embodiments, unstructured document will include primitiveelements (e.g., shapes and images) that are intended to be treated as asingle element but are not defined as such in the document. When suchprimitive elements occupy a compact and isolated area of a document,they can be associated using a novel cluster analysis technique referredto as bounds clustering. The objective of bounds clustering, in someembodiments, is to minimize the spread of a cluster, where the spread iscalculated from the bounds of the collection of primitive elements(e.g., shapes) in the cluster, while simultaneously maximizing thenumber of primitive elements in the cluster. The bounds, in someembodiments, are based on the bounding boxes for a shape or collectionof shapes.

Some embodiments of the invention provide methods for identifying graphs(i.e., graphic objects) of a region that should be joined. These joinedgraphs can then be treated as one object for the purposes of furtherreconstruction. Furthermore, they can be treated as one object whenviewed, selected, zoomed, copied, moved, edited, etc. Some embodimentstreat joined graphs as one object for use in selection, display, andnavigation processes described in further detail in the concurrentlyfiled U. S. Patent Application with Attorney Docket No. APLE.P0144,entitled “Identification of Regions of a Document”, which isincorporated herein by reference.

FIG. 48 conceptually illustrates a process 4800 of some embodiments forjoining individual graphs into joined graphs. Process 4800 will bedescribed in conjunction with FIG. 49. FIG. 49 illustrates the joiningof some, though not all, of several graphs on a page 4900. As shown inFIG. 48, process 4800 receives (at 4805) a portion of a document. Thedocument portion is an entire document, a section of a document, a page,or a zone in some embodiments. Some embodiments perform the graphjoining process for the entire document at once, while some embodimentsperform the process on a zone-by-zone or page-by-page basis.

The process identifies (at 4810) graphs in the document portion. FIG. 49illustrates a page 4900 that includes six graphs: a seven-pointed star4905, a pentagon 4910, an octagon 4915, a cross 4920, a triangle 4925,and a five-pointed star 4930.

The process then uses cluster analysis to join (at 4815) some of theidentified graphs. The process then ends. Some embodiments use a form ofcluster analysis called bounds clustering that is described in detailbelow by reference to process 5000. Some embodiments apply efficiencytechniques described below in Section VI to perform the clusteranalysis. Some embodiments only join graphs when they are close togetherand do not take up too large a portion of a page or zone. FIG. 49illustrates that seven-pointed star 4905 and pentagon 4910 are joinedinto a single graph 4935, and triangle 4925 and five-pointed star 4930are joined into a single graph 4940. Because they are isolated on page4900, octagon 4915 and cross 4920 are not joined either to each other orto any other graphs.

A. Bounds Clustering

FIG. 50 conceptually illustrates a process 5000 of some embodiments forperforming bounds clustering to identify graphs that should be joinedand joining those graphs. In some embodiments, process 5000 takesadvantage of memory and processing efficiencies described below inSection VI (e.g., indirectly sorted arrays, quick partitioning, etc.).As shown, the process receives (at 5005) graphs for a document portion.The document portion is an entire document, a section of a document, apage, or a zone in some embodiments.

The process then determines (at 5007) whether there are at least twographs in the document portion. When there are one or zero graphs, thereis no reason to perform clustering to attempt to join graphs, thereforethe process ends. Otherwise, the process sorts (at 5010) the graphs bydrawing order. The drawing order, in some embodiments, is the sequencein which objects are drawn on a page. Often, when multiple objects areintended to be treated as a single object, they will be drawn insequence. Some embodiments, however, sort based on other heuristics,such as the location of the object on the page.

Next the process sets (at 5015) the first graph in the drawing order asthe current graph g. The process then determines (at 5020) whether g isthe last graph in the document portion. When g is the last graph, thenno spread between graph g and a next graph can be calculated, so theprocess proceeds to 5040 which is described below.

Otherwise, when the graph g is not the last graph,.the processcalculates (at 5025) a spread between the graph g and the next graph inthe drawing order, and stores (at 5030) the calculated spread in anarray. A spread, in some embodiments, is a measure of how close togethertwo objects are to each other. Some embodiments use the bounding boxesof the two objects to calculate the spread. For example, someembodiments calculate the spread of a set of graphic objects is as thesum of the width and the height of the smallest upright bounding boxinto which the set of objects fits, divided by the sum of the width andheight of the page.

FIG. 51 illustrates two pages 5101 and 5102, each having two graphicobjects for which the spread is calculated. Page 5101 includes twographic objects 5105 and 5110, while page 5102 also includes two graphicobjects 5115 and 5120 having the same shapes and sizes as objects 5105and 5110, but located at different places on the page. FIG. 51 alsoillustrates the smallest bounding box 5125 for objects 5105 and 5110 andthe smallest bounding box 5130 for objects 5115 and 5120. Using themetric to calculate spread mentioned above, the spread for objects 5105and 5110 is (X_(S1)+Y_(S2))/(X_(p)+X_(Y)), while the spread for objects5115 and 5120 is (X_(S2)+Y_(S2))/(X_(p)+X_(Y)). Some embodiments insteadcalculate the spread as the area of the bounding box for the collectionof objects divided by the area of the page. Some embodiments use metricsthat do not relate to the page size such as the size of the bounding boxfor the collection of objects compared to the individual bounding boxesof the objects themselves.

Next, the process (at 5035) sets the next graph as the current graph g.The process then proceeds to 5020 which was described above. Once allthe spreads have been calculated, the process uses (at 5040) the spreadsas first-order differences for difference clustering in order to defineclusters of graphs. Some embodiments perform difference clustering asdescribed in the above mentioned U. S. Publication No. 2007/0250497. Asdifference clustering of some embodiments only requires the differencesbetween the input values, and does not require the actual values of theinputs, the spreads can be used as the first-order differences despitenot arising as actual differences. Clusters that result from differenceclustering will, in some embodiments, have relatively small spreadsbetween consecutive graphs in the same cluster as compared to thespreads between graphs in different clusters.

One of ordinary skill in the art would recognize that the spread, andthus the concept of bounds clustering, is not limited to graphic objectson a page. For example, spreads can be calculated amongthree-dimensional objects (by using volumes rather than areas or bysumming over the bounding boxes in three dimensions rather than two),and thus be used to cluster three-dimensional objects (e.g., in athree-dimensional media-editing application such as a video compositingapplication).

After difference clustering is used, with the spreads as first-orderdifferences, clusters of graphics are defined. Process 5000 selects (at5045) a cluster C from the unevaluated clusters. The process thenprocesses (at 5050) C into a set of subsequences of graphs that meetcertain constraints. Different embodiments use different constraints todefine the joined graphs.

Some embodiments impose the requirement that the objects in asubsequence must be consecutive in drawing order. Some embodimentsrequire that the objects in a sequence be mutually overlapping in thatthere is no way to partition the cluster into two nonempty subsequences,each of which is consecutive in drawing order, such that the uprightbounds of the group of objects in the first partition is disjoint fromthe upright bounds of the group of objects in the second partition. Athird requirement imposed by some embodiments is that each subsequencemeets density constraints, which ensure that each subsequence includes asufficient number of graphs (e.g., two) with a sufficiently small totalspread.

Some embodiments use modified versions of the above conditions. Forexample, instead of the upright rectangular bounds, some embodiments usetighter bounds such as a path around the non-transparent pixels of animage. In some embodiments, the collection of objects in each of thesesubsequences is joined as a single graph.

Process 5000 next determines (at 5055) whether there are more clustersto evaluate. When more clusters remain, the process proceeds to 5045 toselect another cluster and process that cluster into subsequences.Otherwise, when all clusters have been processed, the process ends. Nowthat the graphs are joined, they can be treated as one object whenviewed, selected, zoomed, copied, moved, edited, etc. Some embodimentstreat joined graphs as one object for use in selection, display, andnavigation processes such as described in detail in the concurrentlyfiled U. S. Patent Application with Attorney Docket No. APLE.P0167,entitled “Selection of Text in an Unstructured Document”, which isincorporated herein by reference.

B. Processing Clusters into Subsequences

As noted above, after clusters of graphs have been identified, someembodiments process each cluster into subsequences to identify the finaljoined graphs (and then associate the primitive elements that make upeach joined graph). FIG. 52 illustrates a process 5200 of someembodiments for processing a cluster into subsequences. In someembodiments, process 5200 is performed at operation 5050 of process5000, for each cluster.

As shown, process 5200 receives (at 5205) a cluster of graphs. As noted,in some embodiments, this cluster is the output of bounds clusteringthat uses spreads as the first order differences for graphs that areordered by drawing order. The process then determines (at 5207) whetherthe cluster is empty (i.e., does not include any graphs). When thecluster is empty, the process ends. Otherwise, the process selects (at5210) the first graph in the cluster that is not yet in a subsequence.In some embodiments, the cluster is ordered by drawing order, such thatthe first time through operation 5210 the selected graph is the firstgraph in the cluster that is drawn in the document that includes thegraphs.

The process then defines (at 5215) a new subsequence that includes theselected graph (at this point, the selected graph is the only graph inthe subsequence). The new subsequence has the bounds of the selectedgraph. In some embodiments, the bounds of the selected graph is thesmallest upright bounding box that includes the graph. Other embodimentsdefine the bounds of the graph differently, e.g. using the smallest-areapath that completely encloses all of the non-transparent pixels of thegraph.

Next, process 5200 determines (at 5220) whether there are more graphs inthe cluster. When there are no more graphs in the cluster, the processproceeds to 5245, which is described below. Otherwise, the processselects (at 5225) the next graph in the cluster. In some embodiments,the next graph in the cluster is the next graph in the drawing orderthat is in the cluster.

The process determines (at 5230) whether the bounds of the new graph(i.e., the graph selected at 5225) intersect with the bounds of thecurrent subsequence. As noted above, different embodiments define thebounds of a graph differently. The bounds of a subsequence that includesmultiple graphs is described below. When the bounds of the new graph donot intersect the bounds of the current subsequence, process stores (at5240) the current subsequence (e.g., in a list of subsequences) andproceeds to 5210, which is described above, to begin the nextsubsequence. The next subsequence begins with the graph recently testedat 5230, because this is the first graph in the cluster that is not yetin a subsequence.

When the bounds of the new graph (selected at 5225) intersect the boundsof the current subsequence, the process adds (at 5235) the new graph tothe subsequence and modifies the bounds of the subsequence to be theintersection of the previous subsequence bounds and the bounds of thenewly added graph. The process then proceeds to 5220, described above,to continue attempting to add graphs to the subsequence.

In some embodiments, the bounds of a subsequence including multiplegraphs is the smallest upright bounding box that includes all of thegraphs. In other embodiments, the bounds is the union of all of theupright bounding boxes for the graphs in the subsequence (in suchembodiments, the bounds of the subsequence will not necessarily berectangular). In some embodiments that define the bounds of a graph asthe smallest-area path including all of the non-transparent pixels ofthe graph, the bounds might be such a path around all of the graphs inthe subsequence or could be the union of such paths for each graph inthe subsequence.

Once all graphs in the cluster have been placed in initial subsequences,the process selects (at 5245) a first subsequence S1. In someembodiments, each subsequence includes graphs that are contiguous in thedrawing order and the subsequences are arranged based on the drawingorder such that the first subsequence is that with the first graphs inthe drawing order.

The process then determines (at 5250) whether there are moresubsequences (i.e., the first time through the process determineswhether there is only one subsequence or not). When there are no moresubsequences, the process ends. Otherwise, the process selects (at 5255)a next subsequence S2.

Next, process 5200 determines (at 5260) whether the bounds of S1 and S2intersect. As described above, the bounds of the subsequences aredefined differently in different embodiments (i.e., they are based onupright bounding boxes in some embodiments, paths around thenon-transparent pixels in other embodiments, etc.). When the bounds ofS1 and S2 do not intersect, the process defines (at 5265) S2 to be S1and proceeds to 5250 to test the next subsequence against the originalS2.

When the bounds do intersect, the process merges (at 5270) the twosubsequences and proceeds to 5245 to select the first subsequence as S1.Some embodiments return to the first subsequence and do not finishprocessing until a set of subsequences that cannot be merged in any wayis run through from the beginning. Other embodiments save processingtime, however, by selecting the subsequence prior to the recently mergedsubsequence as S1 upon returning to 5245 and proceeding from that pointrather than starting over at the first subsequence.

Once the clusters have been processed into subsequences, thesubsequences can be tested against constraints such as the densityconstraints described above. Some embodiments require a particularminimum number of graphs in a subsequence for the graphs to beassociated in a joined graph (e.g., two, five, etc.). Some embodimentsrequired that the spread (calculated as described above) be less than aparticular number (e.g., 0.4, 0.5, etc.).

C. Software Architecture

In some embodiments, the graph joining processes described above areimplemented as software running on a particular machine, such as acomputer, a media player, a cell phone (e.g., an iPhone®), or otherhandheld or resource-limited devices (or stored in a computer readablemedium). FIG. 53 conceptually illustrates a graph joining application5300 of some embodiments for identifying graphs that should be joinedand associating the graphs as one graphic. In some embodiments, theapplication is a stand-alone application or is integrated into anotherapplication (e.g., a document reconstruction application), while inother embodiments the application might be implemented within anoperating system.

FIG. 53 illustrates a graph joiner 5305, a bounds clustering module5310, and a spread calculator 5315, as well as document content 5325.Graph joiner module receives information from the document content 5325.In some embodiments, the information is information about the locationof each graph and the drawing order of the graphs.

The graph joiner 5305 passes information (e.g., locations of graphs andthe position of the graphs in the drawing order) to the spreadcalculator 5315. The spread calculator 5315 of some embodimentscalculates the spread for each successive pair of graphs, and passesthis information to bounds clustering module 5310.

Bounds clustering module 5310 receives information from the graph joiner5305 and the spread calculator 5315 (e.g., an array of spreads to betreated as first order differences) and performs bounds clustering onthe received information. The results of the bounds clustering arepassed back to the graph joiner. In some embodiments, the graph joiner5305 performs further processing of the clusters received from thebounds clustering module to identify whether particular clusters ofgraphs should be associated as single graphs, and returns theassociations to the document content 5325.

In some embodiments, the results of the processes performed by theabove-described modules or other modules are stored in an electronicstorage (e.g., as part of a document object model). The document objectmodel can then be used for displaying the document on an electronicdisplay device (e.g., a handheld device, computer screen, etc.) suchthat a user can review and/or interact with the document (e.g., viatouchscreen, cursor control device, etc.).

VI. Efficient Cluster Analysis

As noted in various sections above, some embodiments of the inventionutilize cluster analysis to perform document reconstruction. Forinstance, alignment guides are identified with the use of densityclustering, joined graphs are identified with the use of boundsclustering, and gaps between characters are used to identify words andsegment gaps with the use of difference clustering. However, clusteranalysis can be very memory-intensive, such that it can be difficult fora resource-limited device, such as a cell-phone or media player, toperform cluster analysis.

Accordingly, some embodiments of the invention provide methods forperforming efficient cluster analysis. In some embodiments, theefficient cluster analysis allows cluster analysis to be performed on aresource-limited device (e.g., a handheld device). Resource-limiteddevices can be limited in terms of available memory, processing power,both, or other computing resources.

In some embodiments, the cluster analysis uses indirectly sorted arraysthat stores indices of an unsorted array. Some embodiments useindirectly sorted arrays to partition data at multiple differentdistance scales concurrently, so as to more quickly find an optimalpartition of the data, as opposed to repeating cluster analysis at eachdifferent distance scale and comparing the results.

FIG. 54 conceptually illustrates a process 5400 of some embodiments forsemantically reconstructing a document using cluster analysis. As shown,process 5400 receives (at 5405) a document on a resource-limited device.In some embodiments, the device is a media player, a cell phone (e.g.,an iPhone®), or other handheld device. The document is a vector graphicsdocument in some embodiments that includes no structural information.

The process then performs (at 5410) efficient cluster analysis on thedocument data on the resource-limited device. For instance, someembodiments perform difference clustering to identify words and segmentgaps, density clustering to identify alignment guides, and boundsclustering to identify compound graphics.

Finally, the process semantically reconstructs (at 5415) the document onthe resource-limited device based on the results of the clusteranalysis. The process then ends. FIG. 55 illustrates a sequence 5500 ofsome embodiments by which a document 5505 is semantically reconstructedon a resource-limited device 5510. The document 5505 is initially parsed(at 5501) into a set 5515 of characters with coordinates. For instance,character 5520 (“r”) has coordinates {X₂, Y₂). Some embodiments alsoparse graphic objects (e.g., images, shapes, etc.)

Next, efficient cluster analysis is applied (at 5502) to the documentdata. In some embodiments, this includes using difference clustering toidentify words, density clustering to identify guides, and boundsclustering to identify graphs to join. Other reconstruction processesare also performed (at 5503). For instance, paragraphs and columns areidentified in some embodiments. One of ordinary skill will recognizethat in some embodiments, the cluster analysis processes and otherreconstruction processes are not necessarily segregated as far as theorder they are performed. The result of the efficient cluster analysisand other reconstruction processes is a semantically reconstructeddocument 5525 that can be displayed, navigated, etc.

A. Cluster Analysis as a Set of Operators

Some embodiments perform cluster analysis (whether it be differenceclustering, density clustering, or bounds clustering) based on severaloperators that are applied to sequences of real numbers (r₁, r₂, . . . ,r_(N)). Some embodiments include the following operators:

-   -   A differencing operator D((r₁, r₂, . . . , r_(N)))=(r₂−r₁,        r₃−r₂, . . . , r_(N)−r_(N−1)). The differencing operator D, in        some embodiments, defines a pairwise grouping of the elements        r_(N) (i.e., defines values for the pairs {r₂, r₁}, {r₃, r₂},        etc.    -   A sorting operator S((r₁, r₂, . . . , r_(N)))=(s₁, s₂, . . . ,        s_(N)), where (s₁, s₂, . . . , s_(N)) is a permutation of (r₁,        r₂, . . . , r_(N)) such that s₁≦s₂≦ . . . ≦s_(N).    -   A partitioning operator P(g, (r₁, r₂, . . . , r_(N)))=((r₁, . .        . , r_(K1)), (r_(K1+1), . . . , r_(K2)), . . . , (r_(Kp+1), . .        . , r_(KM)), (r_(KM+1), . . . , r_(N)), where r_(J+1)−r_(J)≧g if        and only if J is in the set {K₁, . . . K_(M)). In some        embodiments, the variable g is called a gap minimum, and the        operator P partitions the sequence (r₁, r₂, . . . , r_(N)) into        non-overlapping subsequences everywhere that the difference        between two subsequent values exceeds the gap minimum.    -   A coalescing operator C that operates recursively on a        partitioned sequence (such as the output of the operator P) to        join neighboring pairs of subsequences into a single subsequence        any number of times. In some embodiments, the tests to determine        when to join neighboring pairs are domain-independent.    -   A filtering operator F that operates on a partitioned sequence        to remove some of the clusters based on tests that are domain        independent. The density constraints discussed above in Section        III are an example of the use of F.

Some embodiments of difference clustering are performed in terms of theabove operators. Similarly, because bounds clustering uses differenceclustering with spread values substituted for first-order differences,some embodiments of bounds clustering are performed in terms of theabove operators.

For instance, some embodiments apply the sorting operator S to inputdata, followed by the difference operator D to generate first-orderdifferences. S and D are then applied to the result data to generatesecond-order differences (the differences between the differences). Thesecond-order differences are sorted with S, and the second-orderdifferences are then split into two disjoint subsequences (theintra-level differences and the larger inter-level differences).

In some embodiments, the splitting includes further application of D tothe second-order differences to obtain third-order differences, followedby S to order the third differences. The split in second-orderdifferences generally occurs where there is one third-order differencesubstantially larger than the rest. Some embodiments evaluatedomain-specific factors as well.

Once the split is established, some embodiments apply P using a gapminimum equal to the smallest inter-level second difference to partitionthe ordered first differences, such that each partition represents alevel of clustering. Some embodiments apply C to this partition, whilesome may not. To partition the data into clusters at a particular level,some embodiments apply P to the (sorted) input data using a gap minimumequal to the smallest difference at the particular level. Someembodiments apply C at this point as well, though often with differentcriteria for coalescing the cluster partition than for the levelpartitions. Lastly, some embodiments apply F to disqualify some of theclusters.

Some embodiments of density clustering are also performed in terms ofthe above operators. For example, some embodiments apply S followed by Dto the input data to generate first-order differences, and apply S tosort the differences. For each of the differences d, some embodimentspartition the ordered input data with the operator P using a gap minimumd, then filter the partitions using density constraints. Each of thepost-filtering partitions is measured by an optimization metric and theoptimal partition is selected as the final clustering. Some embodimentsloop through the first-order differences (as gap minimums) starting withthe largest and moving to successively smaller values in the sortedsequence.

In some embodiments, the loop can be ended early for efficiency if thereis enough information. Specifically, some embodiments recognize thateach successive partition will be the previous partition with one of theclusters split into two clusters. Some embodiments also recognize thatclusters that do not meet a minimum size density constraint will nevermeet such a constraint in the future, so these clusters can bediscarded. Once all clusters in a partition have fallen below theminimum size, then the loop is ended prematurely in some embodiments.

B. Efficient Data Structures for Cluster Analysis

Some embodiments perform efficient cluster analysis by using efficientdata structures that allow for memory and processing savings. Forinstance, when sorting data (e.g., applying the operator S to inputdata), rather than generating a new array for the data, some embodimentsdefine an array of indices into the array of unsorted data, with theindices sorted in order of the values they reference. This is referredto as an indirectly sorted array in some embodiments. One of ordinaryskill in the art will understand that while the examples use arrays, anyother suitable data structure may be used as well.

FIG. 56 conceptually illustrates a process 5600 of some embodiments forpartitioning a data set by using indirectly sorted arrays. Process 5600will be described in conjunction with FIG. 57. FIG. 57 illustrates thepartitioning of a data set with nine data items (0.00, 7.43, 17.14,25.46, 26.60, 30.35, 34.25, 39, and 46.97). As shown in FIG. 56, process5600 receives (at 5605) a sorted array A with data values to beclustered. In some embodiments, the data is character location data foridentifying words in a document or identifying alignment guides.Referring to FIG. 57, the data set is stored in a sorted array A 5710,with indices A[0] -A[8].

Next, process 5600 next defines and stores (at 5610) an array D(A) offirst-order differences of the array A by comparing pairs of subsequentvalues of array A. In some embodiments, the array D(A) is generated byuse of the operator D that is described above in subsection A. FIG. 57illustrates the array D 5715 that stores the first-order differencesbetween the data. For instance, the value in index D[3] is the value inindex A[3] subtracted from the value in index A[4] of array A 5710.

Next, the process defines and stores (at 5615) an indirectly sortedarray S(D(A)) of the indices of D(A) by applying a sort function to thearray D(A). In some embodiments, the sort function is the operator Sthat is described above in subsection A. FIG. 57 illustrates theindirectly sorted array S(D) 5720 that sorts the values of array D 5715.The first value in the array 5720 (“3”) references index 3 of array D5715, which is the smallest of the first-order differences (“1.14”). Thesecond value in the array 5720 references index 4 of array D 5715, whichis the second smallest first-order difference, and so on.

The process then determines (at 5620) the minimum size of the gapsbetween clusters to be used in partitioning the data. In someembodiments, this is the gap minimum g for use with the partitioningoperator P described above in subsection A. The minimum gap size isspecified by a user in some embodiments, or is a value inherent to theproblem being solved in others. Some embodiments use multiple partitions(e.g., in the case of density clustering) such that different gapminimums based on the data are used.

Next, process 5600 partitions (at 5625) the data into clusters usingconsecutive indices stored in the array S(D(A)). The process then stores(at 5630) the partition. The process then ends. Some embodiments use theindices stored in the indirectly sorted array to partition the data. Insome embodiments, the index stored in S(D(A)) corresponding to thesmallest first-order difference that is larger than the gap minimum(i.e., the effective gap minimum) will correspond to the index in thesorted array of data after which the data should be split. All indicesstored in the array S(D(A)) after the effective gap minimum will alsoindicate where to split the sorted data, because they represent gapslarger than the gap minimum.

FIG. 57 illustrates that the effective gap minimum is 7.97 in thisexample, which is in index 7 in array D 5715. Thus, the partition 5725of data has four clusters, because it is split in three places (afterindexes 7, 2, and 1). Some embodiments store the partition as a singleindex of the array S(D(A)). The partition 5725 is stored as index 5730,which has a value of 5. This indicates that the index corresponding tothe effective gap minimum is stored at index 5 of array 5720, andtherefore the indices for partitioning the data are stored at indices 5and up of array 5720.

The above process 5600 enables multiple processing and memoryefficiencies for cluster analysis. First, storing the indices (which areintegers) rather than the decimal values of the actual data in thesorted array of differences saves memory space. Second, instead ofactually storing the partition as multiple separate arrays, it is storedas a single integer value referencing an index of the indirectly sortedarray, which can bring about substantial memory savings when there arenumerous partitions being evaluated for large arrays of data. Third, theindices at which to partition the data can be read off quickly from theindirectly sorted array, which substantially saves processing time.

These efficiencies can be leveraged in numerous ways to perform clusteranalysis. FIG. 58 conceptually illustrates a process 5800 of someembodiments for performing cluster analysis at multiple distance scalesconcurrently. In some embodiments, process 5800 takes advantage of theefficiencies offered by process 5600. As shown, process 5800 defines (at5805) an indirectly sorted array of differences of data values to beclustered. This is an array such as array 5720 of FIG. 57, and isarrived at in some embodiments by sorting the input data values, takingthe first-order differences, and then sorting those.

Process 5800 then partitions (at 5810) the data values at severaldifferent distance scales concurrently. In some embodiments, this meansthat multiple partitions are generated for the data using different gapminimums. For instance, in the case of density clustering, each possiblepartition is generated in some embodiments. In some embodiments, becausethe first-order differences are sorted with an indirectly sorted array,the partitioning locations for the data can be quickly read off as theindices stored in the indirectly sorted array.

Next, the process stores (at 5815) each partition as an integer valuereferring to an index of the indirectly sorted array. Integer value 5730of FIG. 57 is an example of storing a partition as a single integervalue. The process then determines (at 5820) the optimal distance scale(and thus the optimal partition). For example, some embodiments use anoptimization measure such as is described for density clustering abovein Section III. Furthermore, some embodiments eliminate some of theclusters in a partition by using constraints before testing thepartition against the optimization measure.

Finally, once the optimal distance scale is determined, the processstores (at 5825) the partition of data derived from the optimal distancescale as the set of clusters for the problem being solved. The processthen ends. In some embodiments, the set of clusters is stored as a newarray once it is determined that it is the optimal set.

While the above descriptions indicate the efficiencies gained forrepeated use of the partitioning operator, the memory and processingefficiencies from indirectly sorted arrays and storing a partition as asingle value are applicable to other aspects of cluster analysis aswell. For instance, the coalescing operator can take advantage of thesame efficiencies in some embodiments.

As noted above, the coalescing operator C of some embodiments joinsneighboring clusters in a partition, possibly repeatedly. The joining ofneighboring clusters can be represented as removing a split in apartition. Because each of these splits corresponds to one of theconsecutive indices in an indirectly sorted array, coalescing clusterscan be defined as disqualifying particular indices from the sequence. Assuch, the results of applying the coalescing operator to a partition canbe a sequence (e.g., an array) of qualifying indices (i.e., indices atwhich the new partition is split). Storing such a subsequence is muchfaster in some embodiments than directly moving around the data in theclusters being coalesced.

Furthermore, coalescing clusters of differences (which is effectively acombination of levels of differences) does not adversely affect theefficiency with which the data clusters (as opposed to the differenceclusters) can be quickly read off for a particular chosen level. Evenafter coalescing the clusters of differences, the indices in the L-thindirectly sorted cluster of differences and above are the split pointsfor the data clusters at level L. The change due to coalescing is thatthere will be fewer indirectly sorted second differences that determinewhere each indirectly sorted first difference cluster starts.

Because the filtering operator (which eliminates clusters of data basedon constraints) is only applied to clusters of data (not to clusters ofdifferences), the data clusters have already been determined when thefiltering operator is applied, and thus it does not interfere with theefficiencies gained through the above implementations of thepartitioning and coalescing operators.

Efficiencies can also be gained in the splitting of second differencesinto intra-level and inter-level second differences that is performed indifference clustering, as described above in Section IV. In someembodiments, the conditions used to determine a split point may dependon the clustering of first differences and the data that would result.Thus, the evaluation of these conditions benefits directly from theefficiencies in determining partitions of differences (and thuspartitions of data).

For instance, in the case of difference clustering as applied todocument reconstruction, the splitting of second differences is used todetermine word breaks and segment breaks (e.g., column, tab, etc. gaps)on a text line, which correspond to first order differences and greaterthan first order differences respectively. In some embodiments, the goalis to split the second differences such that the minimum of the secondcluster of first differences is not much smaller than the expected spacecharacter width for the applicable font. Furthermore, a secondary goalwould be that the data clusters (each of which is a word) have anaverage size typical for words in the applicable language. Potentialsplit points can be assessed comparatively lower depending on how farthe resulting clusters of first differences and the clusters of datawould differ from these expectations. Such assessments can be combinedin some embodiments with other measures applied directly to the seconddifferences (e.g., the relative size of the split, the percentile of thesplit position, and the percentage increase at the split) in a formulathat determines the optimal split point. The repeated testing ofdifferent splits in the second differences can be made significantlymore efficient by the processes described above.

One of ordinary skill in the art will recognize that while clusteranalysis and the specific efficiency techniques described above haveprimarily been described with respect to its use in documentreconstruction, they are applicable to any problem in which there is aset, a distance function on pairs of elements of the set, and a need toidentify subsets of elements separated by distances that are small interms relative to the set. For instance, cluster analysis can be appliedto analyzing user interaction with an application, web page, or video,by clustering position data acquired by measuring eye movements, mousemovements, or touch screen interactions. As another example, a rasterimage (i.e., bitmap) can be compressed by reducing the number of colorsused to encode it. Cluster analysis can be used on the original set ofcolors to select a reduced set of colors, such that each cluster ofcolors is replaced by a single color (often equal to an average of itsmembers). Still another example is that some image recognitiontechniques (e.g., biometrics, optical character recognition, currencyvalidation, etc.) and vectorization of raster images depend onclustering of pixels in a metric space defined by spatial and colorcoordinate axes. As a final example, patterns in experimental data(e.g., scientific or business data) are often found by plotting datapoints in a space the axes of which are the parameters of interest.Cluster analysis can be applied to this data, noting that all points ina given cluster have approximately the same values of all parameters ofinterest.

C. Software Architecture

In some embodiments, the cluster analysis described above is implementedas software running on a particular machine, such as a computer, a mediaplayer, a cell phone (e.g., an iPhone®), or other handheld orresource-limited devices (or stored in a computer readable medium). FIG.59 conceptually illustrates the software architecture of a clusteranalysis application 5900 of some embodiments for performing clusteranalysis. In some embodiments, the application is a stand-aloneapplication or is integrated into another application (e.g., a documentreconstruction application), while in other embodiments the applicationmight be implemented within an operating system.

Cluster analysis application 5900 includes density clustering module5905, difference clustering module 5910, and bounds clustering module5915. The application also includes sorting module 5920, differencingmodule 5925, partitioning module 5930, coalescing module 5935, andfiltering module 5940, as well as cluster analysis storage 5945.

FIG. 59 also illustrates document content 5950. One of ordinary skillwill recognize that cluster analysis application 5900 could be used forother processes that use cluster analysis that are not related todocument reconstruction. Density clustering module 5905, differenceclustering module 5910, and bounds clustering module 5915 all receiveinformation (e.g., primitive element position data) from documentcontent 5950. Density clustering module 5905 performs density clusteringas described above in Section III, in part by using the modules5920-5940. Difference clustering module 5910 performs differenceclustering as described above in Section IV, in part by using themodules 5920-5940. Bounds clustering module 5915 performs boundsclustering as described above in Section V, in part by using the modules5920-5940. The output of modules 5905-5915 is returned to the documentcontent 5950.

In some embodiments, the five modules 5920-5940 perform operationsassociated with the five operators described above in subsection A. Thesorting module 5920 of some embodiments receives data from one of themodules 5905-5915 and orders the data (e.g., from lowest value tohighest value). The differencing module 5925 of some embodimentsreceives data from one of the modules 5905-5915 and determines thedifferences between adjacent pieces of data. The partitioning module5935 of some embodiments receives data from one of the modules 5905-5915and partitions the data into multiple subsets. The coalescing module5935 of some embodiments receives data as multiple subsets from one ofthe modules 5905-5915 and joins adjacent subsets according to variousconditions. The filtering module 5940 of some embodiments receives apartitioned sequence of data in some embodiments and filters outpartitions based on various constraints.

The modules 5920-5940 store data in cluster analysis storage 5945, aswell as pass the data back to the modules 5905-5915. In someembodiments, the sorting module 5920 stores its results in clusteranalysis storage 5945 as a sorted array of indices (i.e., an indirectlysorted array). The partitioning module, in some embodiments, storespartitions in the cluster analysis storage 5945 as a single integervalue referencing an index of an indirectly sorted array.

VII. Efficient Data Structures for Parsing and Analyzing a Document

Some embodiments of the invention provide novel methods and datastructures that enable more efficient parsing and analysis of adocument. Some embodiments provide an application programming interface(API) that minimizes redundant copies of data as the data ismanipulated. An API, in some embodiments, is a set of functions,procedures, methods, classes, or protocols that an operating system,library, service, or framework provides to support requests made bycomputer programs. In some embodiments, the API is statically linked,while in other embodiments an API is dynamically linked.

Typically, APIs return copies of internal data or give read-only accessto internal data which must then be copied before being manipulated inany way. This creates many layers of redundant data, which slowsprocessing and consumes excess memory. Some embodiments solve thisproblem by decoupling objects from their data so that object APIs can bemade optimal for a programmer at the same time that the data structuresare made optimal with respect to performance and memory consumption.Some embodiments use such an API for reconstructing a document asdescribed above in Sections II-VI as well as in the concurrently filedU. S. Patent Application with Attorney Docket No. APLE.P0144, entitled“Identification of Regions of a Document”, which is incorporated hereinby reference. However, one of ordinary skill in the art will recognizethat such an API can be used for any sort of analysis of parsed inputdata.

Some embodiments provide an API that appears to a user (e.g., aprogrammer or a software application using the API) as if the user hastheir own independent, modifiable copy of the class members of the APIwith no explicit restrictions. In other words, it appears to the user asthough any object returned through the API is completely modifiable bythe user. However, in some embodiments, the objects will actually onlycopy themselves when absolutely necessary, and in most cases will managememory in such a way as to minimize the amount of memory actually used.The memory management of some embodiments is done by using a sortedarray of pointers that has a shared memory object which keeps track ofthe use of the pointers by other objects. In some embodiments, numerousobjects can all reference the same pointer array through the sharedmemory object, enabling substantial memory savings as compared to makingcopies of the data at every stage of analysis. One of ordinary skill inthe art will recognize that while pointers are used to describe certainfeatures below, any sort of referential data structure could be used.

A. Document Reconstruction with Shared Pointers

Some embodiments use an API such as is described above to reconstruct adocument. FIG. 60 conceptually illustrates a process 6000 of someembodiments for reconstructing a document efficiently. Process 6000 willbe described in conjunction with FIG. 61. FIG. 61 illustrates a sequenceby which a document 6100 is parsed and analyzed according to process6000.

As shown in FIG. 60, process 6000 receives (at 6005) a portion of adocument. In some embodiments, the document portion is a page, and theprocess operates on a page-by-page basis. In other embodiments, thedocument portion is an entire document, a section of a document, or azone on a page. The process then parses (at 6010) the document todetermine the characters in the document portion, and stores (at 6015)an array of characters for the parsed data.

FIG. 61 illustrates that the document 6100 is parsed into arandomly-ordered array 662 of characters. While these examples usearrays, one of ordinary skill will understand that any other suitabledata structured may be used. In some embodiments, parsing the documentinvolves reading a stream of bytes representing the document and turningthat stream into a usable representation (such as the character array)of the information in the stream. The characters in the stream are readin a random order in some embodiments, which is why the order of thearray 662 is random. The characters of some embodiments have coordinatesand/or page numbers. In some embodiments, each character is stored as anobject that includes the associated coordinate or page number values.

Process 6000 defines (at 6020) a sorted array of pointer that orders thecharacters for the document portion. In some embodiments, the charactersfor a page are sorted with a primary sort of top to bottom and asecondary sort of left to right. Some embodiments that store multiplepages in a character array sort by page first. FIG. 61 illustrates anarray of pointers 6110 that is defined for the sorted characters. Thefirst pointer 6111 points to the letter “L” in the array 662, the secondpointer 6112 to the letter “o”, and so on. Defining an array of pointersto the initial character array rather than defining and storing aseparate new array saves memory in some embodiments.

The process next receives (at 6025) instructions to manipulate stringobjects. Some embodiments define a string object as a pointer to alocation in the sorted array of pointers and a count of how manycharacters are in the string. For instance, a string object for theentire page would point to the first pointer in the sorted pointer array(the top-leftmost character), and give a count of the number ofcharacters on the page.

In some embodiments, the instructions include splitting strings, joiningstrings, adding characters, removing characters, and re-orderingcharacters. These operations, in some embodiments, are invoked as partof the process of reconstructing a document and using the reconstructeddocument as described above in Sections II-VI as well as in theconcurrently filed U. S. Patent Application with Attorney Docket No.APLE.P0144, entitled “Identification of Regions of a Document”, which isincorporated herein by reference. For instance, in some cases when linesare merged, the order of characters must be modified. When zones aredefined, some embodiments define strings for each zone, which in manycases involves splitting strings, joining strings, or both.

After receiving the instructions, the process determines (at 6030)whether the instructions can be performed using only pointers that arealready allocated (e.g., the sorted pointer array defined at 6020). Insome embodiments, splitting strings involves only the use of pointersthat are already allocated. In the case of document reconstruction, someprocesses only involve the splitting of strings (e.g., lineidentification, line splitting, etc.). Furthermore, joining strings thatare next to each other in the sorted array of pointers will involve onlythe use of already-allocated pointers in some embodiments.

FIG. 61 illustrates how identifying the two lines in document 6100results in two string objects 6115 and 6120 that reference thealready-allocated pointers in the array 6110. The first line is definedby a string object 6115 that points to the pointer to L 6111 and has acount of 15 (the number of characters on the first line). The secondline is defined by a string object 6120 that points to the pointer to s6113 and has a count of 7 (the number of characters on the second line).In order to define these lines, no new pointers need to be allocated.Over the hundreds or thousands of operations that may be involved inreconstructing a document, this can introduce large memory andprocessing time (because no arrays need to be searched) efficiencies.

The same pointers 6110 can then be used when words are identified. Forexample, string objects 6125 and 6130 define two of the words indocument 6100. These words point to the same start pointers as stringobjects 6115 and 6120, but have different counts because the words areshorter than the lines. However, no new pointers need to be allocated todefine these words, only new string objects. For a full document,hundreds or thousands of different string objects may all reference thesame pointer array (such as pointers 6110), introducing large memorysavings over repeatedly allocating memory for new pointer arrays.

When the received instructions can be performed using only pointers thatare already allocated, process 6000 performs (at 6035) the instructionsusing the shared pointers that are already allocated in memory. Theprocess then proceeds to 6055, which is described below. Otherwise, theprocess determines (at 6040) whether the instructions can be performedusing a new collection of pointers.

Some embodiments allocate new pointers when instructions cannot beperformed with only pointers that are already allocated, but theinstructions do not require direct data manipulation of the characterarray. In some embodiments, joining strings that are not next to eachother in a shared array of pointers requires a new allocation ofpointers, because a string object for the joined strings cannot berepresented by pointing to one pointer in the sorted array and movingforward in that array. For instance, referring to FIG. 61, if anoperation called for appending the first line to the end of the secondline, then string object for the appended lines could not point to array6110. Instead, a new array of pointers would have to be allocated in therequired order.

When the received instructions can be performed using a new allocationof pointers, the process performs (at 6045) the instructions by using anew allocation of pointers to the character array. The process then andproceeds to 6055, which is described below. Otherwise, the processperforms (at 6050) the instructions by using a new copy of a portion orthe entire character array. Directly editing the document data (i.e., auser adding a word to the document) is an example of instructions thatcould not be performed without manipulating the actual array ofcharacters in some embodiments. However, a user adding a word to thedocument would not require a completely new copy, but instead could behandled by adding characters to the array and then defining a new arrayof pointers to the characters. Similarly, merging text lines oftenrequires a new array of pointers, because a character from one text linemay be inserted into the next text line, thereby altering the order ofthe characters relative to each other.

Next, the process determines (at 6055) whether more instructions tomanipulate the string objects have been received. When more instructionshave been received, the process proceeds to 6030, which is describedabove, to determine the most efficient way of performing theinstructions. Otherwise, the process ends. This process illustrates thehierarchy of memory and processing savings that are introduced by usingthe shared data. The original data is shared among various pointerarrays, and each pointer array is shared among many string objects. Foreach set of instructions received (e.g., each call into an API), themost efficient way of performing the instructions is used. Ideally, theinstructions will not require the creation of any new pointers, and onlynew string objects need be created. If this is not possible, then memorysavings may still be gained by creating new pointers that share theoriginal data as opposed to creating a new character array.

Although process 6000 has been described with reference to stringobjects and specifically string objects for document reconstruction, oneof ordinary skill in the art will recognize that the efficiencies gainedby exhibiting a preference for using already-allocated pointers and thenfor allocating new pointers as opposed to copying data, are applicableto a wide range of problems where memory and processing time are at apremium.

B. Shared Memory Objects

In some embodiments, each array of pointers has a shared memory objectthat manages the use of the pointers in the array. In some embodiments,the shared memory object for a particular pointer array keeps track ofthe data objects (e.g., string objects) that reference the particulararray. In some embodiments, the shared memory object also keeps track ofwhere in memory the pointer array starts as well.

FIG. 62 illustrates the manner in which data is stored according to someembodiments of the invention. FIG. 62 illustrates an array of data 6205,a sorted array of pointers 6210, a shared memory object 6215, and dataobjects 6220. The data array 6205 is randomly-ordered parsed data insome embodiments (e.g., character data from a parsed document).

The sorted array of pointers 6210 is an array of pointers to the dataarray 6205. Each pointer points to a data item in the array 6205 in someembodiments. The pointers are arranged in an order based upon a sort ofthe data. For instance, in the case of a document, the pointers arearranged in the reading order of the characters to which they point insome embodiments.

Each of the data objects 6220 includes a reference to a location in thepointer array 6210 and a count. The location in the pointer array 6210for a particular data object is the pointer that points to the firstpiece of data that the data object references. For instance, when thedata object is a string object for the word “Array”, the data objectwould specify the location in the pointer array where the pointer thatpoints to the “A” is found. The data object would also include a countof 5.

FIG. 62 also illustrates a shared memory object 6215. In someembodiments, the shared memory object manages the use of the sortedarray 6210 by the data objects 6220. The shared memory object 6215 keepsa count of the number of data objects 6220 that reference the array6210.

Some embodiments do not define the shared memory object 6215 when afirst data object (that points to the start of the array and has a countof the entire array) is defined. However, once a second data objectpoints to the array, the array is now shared, and the shared memoryobject 6215 is defined to keep track of how many data objects share thearray and where the start of the array is, as each individual objectdoes not have this information. Accordingly, in some embodiments, thedata objects 6220 can call a function to instantiate a shared memoryobject for a pointer array if none exists when the data object is set topoint to the pointer array. When the number of objects 6220 drops tozero, the shared memory object 6215 deallocates the pointers 6210 and isthen itself removed from memory.

In some embodiments, each individual data object 6220 sharing thepointer array 6210 does not have any knowledge that other objects 6220are also using the pointers in array 6210. Furthermore, the objects 6220do not have any knowledge of the start or end of array 6210, merelyreferencing some point in the array 6210. However, the shared memoryobject 6220 of some embodiments knows where the start of the array is inmemory.

C. Software Architecture

In some embodiments, the API described above are implemented as softwarerunning on a particular machine, such as a computer, a media player, acell phone (e.g., an iPhone®), or other handheld or resource-limiteddevices (or stored in a computer readable medium). FIG. 63 conceptuallyillustrates an API 6300 that performs document reconstruction processeswhile using the efficiency techniques described above in subsections Aand B.

API 6300 includes geometric analysis modules, 6310, documentreconstruction modules 6315, and display and interaction modules 6320.The API 6300 is, in some embodiments, the set of functions, procedures,methods, classes, and/or protocols that is provided for use by externalapplications 6305.

The API 6300 receives requests (e.g., function calls) to the publicmethods by external applications 6305. In some embodiments, there arenumerous external applications. For instance, in the case where an APIis provided on a handheld device (e.g., an iPhone®), the externalapplications might be a PDF viewer (e.g., an e-book reader), a wordprocessor (e.g., Microsoft Word, Apple Pages, etc.), a web browser(e.g., Microsoft Internet Explorer, Apple Safari, Mozilla Firefox,etc.), etc.

The various public methods provided by API 6300 call various privatemethods that perform the geometric analysis and document reconstruction,access the document object model, etc. The data (e.g., the primitiveelements that are initially identified by a parser) is stored in thedocument reconstruction data 6325. Although it may appear to theexternal applications that they can access the data (e.g., whilemanipulating characters to identify words, text lines, etc.), in factthe class members that are manipulated by the external applicationsthrough the API are divorced from the actual data by defining the classmembers to only store references to the data, as described above insubsections A and B.

VIII. Overall Software Architecture

In some embodiments, the processes described above are implemented assoftware running on a particular machine, such as a computer, a mediaplayer, a cell phone (e.g., an iPhone®), or other handheld orresource-limited devices (or stored in a computer readable medium). FIG.64 conceptually illustrates the software architecture of an application6400 of some embodiments for reconstructing, displaying, and interactingwith a document. In some embodiments, the application is a stand-aloneapplication or is integrated into another application, while in otherembodiments the application might be implemented within an operatingsystem. In still other embodiments the modules illustrated in FIG. 64are split among multiple applications. For instance, in someembodiments, one application generates the document object model, whileanother application displays the document and interacts with thedocument object model (see full description below).

Application 6400 includes a parser 6410, profiling modules 6420,semantic reconstruction modules 6430, cluster analysis modules 6440,user interaction modules 6450, and display adaptation modules 6460. Theapplication 6400 also includes document data storage 6415, profilestorage 6425, cluster analysis storage 6435, and document object modulestorage 6445. FIG. 64 also illustrates an operating system 6470 thatincludes cursor controller driver 6475, keyboard drive 6480, and displaymodule 6485. In some embodiments, as illustrated, the cursor controllerdriver 6475, keyboard driver 6480, and/or display module 6485 are partof operating system 6470 even when the compositing application is astand-alone application separate from the operating system.

As shown, the parser 6410 receives a document 6405. In some embodiments,the document is an unformatted document that includes vector graphics(e.g., a PDF). The parser 6410 parses the document information andstores the parsed data in the document data storage 6415. In someembodiments, the parsed text data is stored as an array of characters asdescribed in Section XI of the concurrently filed U. S. PatentApplication with Attorney Docket No. APLE.P0166, entitled “EfficientData Structures for Parsing and Analyzing a Document, which isincorporated herein by reference.

The semantic reconstruction modules 6430 reconstruct the document togenerate the document object model 6445 from the document data 6415.Semantic reconstruction modules 6430 perform such processes as zoneanalysis, guide and gutter identification, layout and flowidentification, table identification, and joined graph identification.

The output of the semantic reconstruction modules also is sent to theprofiling modules 6420. Profiling modules 6420 include a profilematching engine that matches hierarchical profiles and inform thesemantic reconstruction modules how to go about performingreconstruction, as described Section VII of the concurrently filed U. S.Patent Application with Attorney Docket No. APLE.P0141, entitled“Content Profiling to Dynamically Configure Content Processing”, whichis incorporated herein by reference.

The semantic reconstruction modules 6410 also pass information to thecluster analysis modules 6440. Cluster analysis modules 6440 performdensity clustering for guide identification, difference clustering forword and segment gap information, and bounds clustering for identifyinggraphs that should be joined, in some embodiments. The cluster analysismodules use the cluster analysis storage 6435 to store arrays andindices as described in Section VI. The results of the cluster analysisare then passed back to the semantic reconstruction modules 6430.

Once the semantic reconstruction modules 6430 have reconstructed thedocument, they store the document object model 6445. Document objectmodel 6445 stores all information about the semantically reconstructeddocument, such as the zone graph populated with content that isdescribed above in Section II.

Display adaptation modules 6460 use the document object model 6445 todetermine how to display the document. For instance, display adaptationmodules of some embodiments perform processes for displaying thedocument on a small-screen device, which are described in detail inconcurrently filed U. S. Patent Application with Attorney Docket No.APLE.P0143, entitled “Identification, Selection, and Display of a Regionof Interest in a Document”, which is incorporated herein by reference.Display adaptation modules 6460 pass the display information to thedisplay module 6485, which governs the actual display on the screen.

User interaction modules 6450 receive input information from the cursorcontroller driver 6475 and keyboard driver 6480. The input informationdirects the user interaction modules 6450 to perform operations on thedocument, such as selections as described detail in the concurrentlyfiled U. S. Patent Application with Attorney Docket No. APLE.P0167,entitled “Selection of Text in an Unstructured Document”, which isincorporated herein by reference, as well as editing of the document. Ifthe document is edited, then the document object model 6445 must bemodified to reflect the edits.

In some embodiments, the results of the processes performed by some ofthe above-described modules or other modules are stored in an electronicstorage (e.g., as part of a document object model). The document objectmodel can then be used for displaying the document on an electronicdisplay device (e.g., a handheld device, computer screen, etc.) suchthat a user can review and/or interact with the document (e.g., viatouchscreen, cursor control device, etc.).

FIG. 65 conceptually illustrates a process 6500 of some embodiments formanufacturing a computer readable medium that stores a computer programsuch as the application 6400 described above. In some embodiments, thecomputer readable medium is a distributable non-volatile electronicstorage medium (e.g., CD-ROM, hard disk, device firmware, etc.).

As shown, process 6500 begins by defining (at 6505) geometric analysismodules, such as modules 110 of FIG. 1. The process then defines (at6510) document reconstruction modules such as modules 120 of FIG. 1.More detailed examples of such modules include, in some embodiments,guide identification module 3005 of FIG. 30, word identification module4715 of FIG. 47, and graph joiner 5305 of FIG. 53. In some embodiments,semantic reconstruction modules 6430 of FIG. 64 include both geometricanalysis modules and document reconstruction modules, though otherembodiments only include one or the other.

Process 6500 then defines (at 6515) a set of hierarchical profiles, suchas profiles 6425. Next, the process defines (at 6520) a set of modulesfor performing cluster analysis. The cluster analysis modules 6440 arean example of such modules. More detailed examples of such modulesinclude sorting module 5920, differencing module 5925, partitioningmodule 5930, coalescing module 5935, and filtering module 5940 of FIG.59. These modules, in some embodiments, identify a pairwise grouping ofnearest primitive elements of an unstructured document, sort thepairwise primitive elements based on an order from the closest tofurthest pairs, and store a single value that identifies which of thepairwise primitive elements are sufficiently far apart to form apartition, and analyzing different partitions in order to identify anideal optimal partition representing an optimal distance scale.

The process then defines (at 6525) modules for adaptively displaying adocument, such as display adaptation modules 6460. Next, process 6500defines (at 6530) modules for receiving user interactions with adocument, such as modules 6450. The process also defines (at 6535) othermodules. For instance, some embodiments include modules for parsing anincoming document (e.g., a document received by the application) or forefficiently using memory and processing time when performing variousdocument reconstruction operations.

Process 6500 then stores (at 6540) the application on a computerreadable storage medium. As mentioned above, in some embodiments thecomputer readable storage medium is a distributable CD-ROM. In someembodiments, the medium is one or more of a solid-state device, a harddisk, a CD-ROM, or other non-volatile computer readable storage medium.The medium may be firmware of a handheld device (e.g., an iPhone®) insome embodiments.

One of ordinary skill in the art will recognize that the variouselements defined by process 6500 are not exhaustive of the modules,rules, and processes that could be defined and stored on a computerreadable storage medium for an application incorporating someembodiments of the invention. Furthermore, it is equally possible thatsome embodiments will include only a subset of the elements defined byprocess 6500 rather than all of them.

In addition, the process 6500 is a conceptual process, and the actualimplementations may vary. For example, different embodiments may definethe various elements in a different order, may define several elementsin one operation, may decompose the definition of a single element intomultiple operations, etc. Furthermore, the process 6500 may beimplemented as several sub-processes or combined with other operationsin a macro-process.

VIII. Computer System

Many of the above-described features and applications are implemented assoftware processes that are specified as a set of instructions recordedon a computer readable storage medium (also referred to as computerreadable medium). When these instructions are executed by one or morecomputational element(s) (such as processors or other computationalelements like ASICs and FPGAs), they cause the computational element(s)to perform the actions indicated in the instructions. Computer is meantin its broadest sense, and can include any electronic device with aprocessor. Examples of computer readable media include, but are notlimited to, CD-ROMs, flash drives, RAM chips, hard drives, EPROMs, etc.The computer readable media does not include carrier waves andelectronic signals passing wirelessly or over wired connections.

In this specification, the term “software” is meant to include firmwareresiding in read-only memory or applications stored in magnetic storagewhich can be read into memory for processing by a processor. Also, insome embodiments, multiple software inventions can be implemented assub-parts of a larger program while remaining distinct softwareinventions. In some embodiments, multiple software inventions can alsobe implemented as separate programs. Finally, any combination ofseparate programs that together implement a software invention describedhere is within the scope of the invention. In some embodiments, thesoftware programs when installed to operate on one or more computersystems define one or more specific machine implementations that executeand perform the operations of the software programs.

FIG. 66 illustrates a computer system with which some embodiments of theinvention are implemented. Such a computer system includes various typesof computer readable media and interfaces for various other types ofcomputer readable media. Computer system 6600 includes a bus 6605, aprocessor 6610, a graphics processing unit (GPU) 6620, a system memory6625, a read-only memory 6630, a permanent storage device 6635, inputdevices 6640, and output devices 6645.

The bus 6605 collectively represents all system, peripheral, and chipsetbuses that communicatively connect the numerous internal devices of thecomputer system 6600. For instance, the bus 6605 communicativelyconnects the processor 6610 with the read-only memory 6630, the GPU6620, the system memory 6625, and the permanent storage device 6635.

From these various memory units, the processor 6610 retrievesinstructions to execute and data to process in order to execute theprocesses of the invention. In some embodiments, the processor comprisesa Field Programmable Gate Array (FPGA), an ASIC, or various otherelectronic components for executing instructions. Some instructions arepassed to and executed by the GPU 6620. The GPU 6620 can offload variouscomputations or complement the image processing provided by theprocessor 6610. In some embodiments, such functionality can be providedusing CoreImage's kernel shading language.

The read-only-memory (ROM) 6630 stores static data and instructions thatare needed by the processor 6610 and other modules of the computersystem. The permanent storage device 6635, on the other hand, is aread-and-write memory device. This device is a non-volatile memory unitthat stores instructions and data even when the computer system 6600 isoff. Some embodiments of the invention use a mass-storage device (suchas a magnetic or optical disk and its corresponding disk drive) as thepermanent storage device 6635.

Other embodiments use a removable storage device (such as a floppy disk,flash drive, or ZIP® disk, and its corresponding disk drive) as thepermanent storage device. Like the permanent storage device 6635, thesystem memory 6625 is a read-and-write memory device. However, unlikestorage device 6635, the system memory is a volatile read-and-writememory, such a random access memory. The system memory stores some ofthe instructions and data that the processor needs at runtime. In someembodiments, the invention's processes are stored in the system memory6625, the permanent storage device 6635, and/or the read-only memory6630. For example, the various memory units include instructions forprocessing multimedia items in accordance with some embodiments. Fromthese various memory units, the processor 6610 retrieves instructions toexecute and data to process in order to execute the processes of someembodiments.

The bus 6605 also connects to the input and output devices 6640 and6645. The input devices enable the user to communicate information andselect commands to the computer system. The input devices 6640 includealphanumeric keyboards and pointing devices (also called “cursor controldevices”). The output devices 6645 display images generated by thecomputer system. The output devices include printers and displaydevices, such as cathode ray tubes (CRT) or liquid crystal displays(LCD).

Finally, as shown in FIG. 66, bus 6605 also couples computer 6600 to anetwork 6665 through a network adapter (not shown). In this manner, thecomputer can be a part of a network of computers (such as a local areanetwork (“LAN”), a wide area network (“WAN”), or an Intranet, or anetwork of networks, such as the internet. Any or all components ofcomputer system 6600 may be used in conjunction with the invention.

Some embodiments include electronic components, such as microprocessors,storage and memory that store computer program instructions in amachine-readable or computer-readable medium (alternatively referred toas computer-readable storage media, machine-readable media, ormachine-readable storage media). Some examples of such computer-readablemedia include RAM, ROM, read-only compact discs (CD-ROM), recordablecompact discs (CD-R), rewritable compact discs (CD-RW), read-onlydigital versatile discs (e.g., DVD-ROM, dual-layer DVD-ROM), a varietyof recordable/rewritable DVDs (e.g., DVD-RAM, DVD-RW, DVD+RW, etc.),flash memory (e.g., SD cards, mini-SD cards, micro-SD cards, etc.),magnetic and/or solid state hard drives, read-only and recordableblu-ray discs, ultra density optical discs, any other optical ormagnetic media, and floppy disks. The computer-readable media may storea computer program that is executable by at least one processor andincludes sets of instructions for performing various operations.Examples of hardware devices configured to store and execute sets ofinstructions include, but are not limited to application specificintegrated circuits (ASICs), field programmable gate arrays (FPGA),programmable logic devices (PLDs), ROM, and RAM devices. Examples ofcomputer programs or computer code include machine code, such as isproduced by a compiler, and files including higher-level code that areexecuted by a computer, an electronic component, or a microprocessorusing an interpreter.

As used in this specification and any claims of this application, theterms “computer”, “server”, “processor”, and “memory” all refer toelectronic or other technological devices. These terms exclude people orgroups of people. For the purposes of the specification, the termsdisplay or displaying means displaying on an electronic device. As usedin this specification and any claims of this application, the terms“computer readable medium” and “computer readable media” are entirelyrestricted to tangible, physical objects that store information in aform that is readable by a computer. These terms exclude any wirelesssignals, wired download signals, and any other ephemeral signals.

While the invention has been described with reference to numerousspecific details, one of ordinary skill in the art will recognize thatthe invention can be embodied in other specific forms without departingfrom the spirit of the invention. For example, some embodiments receivea document in which each page is defined as a single image. However,some embodiments can perform optical character recognition on thedocument to recognize glyphs, and in some cases shapes (e.g., lines,rectangles, etc.), after which point the document can be reconstructed.Also, some embodiments have been described above as performingparticular geometric analysis and document reconstruction operations onparticular primitive elements. However, one of ordinary skill wouldrecognize that the operations could be applied to other sorts ofprimitive elements. For instance, guide identification is described asinvolving the use of density clustering to identify associations of(i.e., to associate, or to define associations of) glyphs forming avertical boundary. However, similar operations could be applied to lookfor clusters of primitive shapes that form boundaries (e.g., dashedlines).

Furthermore, a number of the figures (including FIGS. 3, 8, 9, 12, 15,18, 20, 21, 25, 26, 31, 33, 36, 38, 40, 42, 48, 50, 52, 54, 56, 58, 60and 64) conceptually illustrate processes. The specific operations ofthese processes may not be performed in the exact order shown anddescribed the specific operations may not be performed in one continuousseries of operations, and different specific operations may be performedin different embodiments. Furthermore, the process could be implementedusing several sub-processes, or as part of a larger macro process. Thus,one of ordinary skill in the art would understand that the invention isnot to be limited by the foregoing illustrative details, but rather isto be defined by the appended claims.

1. A computer readable medium storing a computer program which whenexecuted by at least one processor defines structure for a documentcomprising a plurality of primitive elements that are defined in termsof their position in the document, the computer program comprising setsof instructions for: identifying, for a particular set of primitiveelements, a pairwise grouping of nearest primitive elements in the set;sorting the pairwise groupings of primitive elements based on an orderfrom the closest to the furthest pairs; storing a single value thatidentifies which of the pairwise groupings of primitive elements aresufficiently far apart to form a partition; and using the stored singlevalue to identify and analyze the partition in order to definestructural elements for the document.
 2. The computer readable medium ofclaim 1, wherein the computer readable medium is firmware of a handhelddevice.
 3. The computer readable medium of claim 2, wherein the deviceis a cell phone.
 4. The computer readable medium of claim 2, wherein thedevice is a media player.
 5. The computer readable medium of claim 1,wherein the computer program further comprises a set of instructions fordefining a structured document that comprises the structural elementsand the primitive elements.
 6. The computer readable medium of claim 1,wherein: the document comprises a plurality of glyphs; the computerprogram further comprises a set of instructions for associating sets ofglyphs as words; the particular set of primitive elements are leftmostglyphs of words; and the set of instructions for using the stored singlevalue to identify and analyze the partition in order to definestructural elements comprises a set of instructions for defining leftalignment guides for sets of aligned words.
 7. The computer readablemedium of claim 1, wherein: the document comprises a plurality ofglyphs; the computer program further comprises a set of instructions forassociating sets of glyphs as words; the particular set of primitiveelements are rightmost glyphs of words; and the set of instructions forusing the stored single value to identify and analyze the partition inorder to define structural elements comprises a set of instructions fordefining right alignment guides for sets of aligned words.
 8. Thecomputer readable medium of claim 1, wherein the particular set ofprimitive elements are primitive graphic elements and the set ofinstructions for using the stored single value to identify and analyzethe partition in order to define structural elements comprises a set ofinstructions for associating nearby primitive graphic elements asstructural graphic elements.
 9. The computer readable medium of claim 1,wherein the primitive elements are glyphs, and the set of instructionsfor defining structural elements comprises a set of instructions forassociating sets of nearby glyphs as words.
 10. The computer readablemedium of claim 9, wherein the set of instructions for identifying apairwise grouping of nearest primitive elements comprises sets ofinstructions for: identifying a set of glyphs with a common baseline;sorting the set of glyphs based on coordinates of the glyphs in a firstdirection; for each pair of consecutive glyphs in the sorted set,calculating a difference in coordinate values between the glyphs in thepair; and storing the calculated differences in a first array.
 11. Thecomputer readable medium of claim 10, wherein the set of instructionsfor sorting the pairwise primitive elements based on an order from theclosest to the furthest pairs comprises sets of instructions for:sorting the calculated difference values; and storing, in a secondarray, the indices of the first array that correspond to the sorteddifferences.
 12. The computer readable medium of claim 11, wherein theindex of the first array that stores a smallest difference value isstored as the value at the first index of the second array.
 13. Thecomputer readable medium of claim 11, wherein the computer programfurther comprises a set of instructions for determining a minimumdifference between glyphs, wherein the stored single value is an indexin the second array at which the index of the first array storing saidminimum difference is stored.
 14. The computer readable medium of claim13, wherein the minimum difference represents gaps between words. 15.The computer readable medium of claim 13, wherein the set ofinstructions for using the stored value to identify and analyze thepartitions comprises a set of instructions for splitting a third arraythat stores the sorted x-coordinate values at indices of the third arraythat are stored as values in the second array at all indices of thesecond array at and after the index stored as the single value.
 16. Thecomputer readable medium of claim 1, wherein the set of instructions forstoring the single value comprises sets of instructions for storing aplurality of single values that each identify different partitions forthe primitive elements.
 17. The computer readable medium of claim 16,wherein the computer program further comprises analyzing the differentpartitions for the primitive elements in order to identify an idealoptimal partition representing an optimal distance scale.
 18. Thecomputer readable medium of claim 1, wherein the document is anunstructured document.
 19. The computer readable medium of claim 1,wherein the document is a vector graphics document.
 20. The computerreadable medium of claim 1, wherein the document is a portable documentformat (PDF) document.
 21. A method for defining a program for definingstructure for a document, the method comprising: defining a module foridentifying a pairwise grouping of nearest primitive elements in adocument comprising a plurality of primitive elements that are definedin terms of their position in the document; defining a module forsorting the pairwise groupings of primitive elements based on an orderfrom the closest to the furthest pairs; defining a module for storing asingle value that identifies which of the pairwise-grouped primitiveelements are sufficiently far apart to form a partition; and defining amodule for using the stored value to identify and analyze the partitionsin order to define structural elements for the document.
 22. The methodof claim 21 further comprising defining a module for defining astructured document based on the structural elements and the primitiveelements.
 23. The method of claim 22, wherein the structured document isa hierarchical structure in which the structural elements are nodes.